Cell analysis method using quantitative fluorescence image analysis

ABSTRACT

A system for evaluating one or more biochemical markers for evaluating individual cancer risk, cancer diagnosis and for monitoring therapeutic effectiveness and cancer recurrence, particularly of bladder cancer. The system uses automated quantitative fluorescence image analysis of a cell sample collected from a body organ. Cells are treated with a fixative solution which inhibits crystal formation. Cell images are selected and stored as grey level images for further analysis. Cell images may be corrected for autofluorescence using a novel autofluorescence correction method. A neural net computer may be used to distinguish true-positive images from false-positive images to improve accuracy of cancer risk assessment. Cells having images positive for a marker amy be compared to threshold quantities related to predetermined cancer risk.

BACKGROUND

This invention relates to methods for screening cell samples forcytological factors using quantitative fluorescence image analysis, andmore particularly, but not by way of limitation, to a method forscreening cell samples for cytological factors indicative of cancer orfor an increased risk for cancer using quantitative fluorescence imageanalysis.

An estimated 47,000 cases of bladder cancer were diagnosed in 1991.Approximately 10,000 people were estimated to have died from bladdercancer in 1991. Most of the deaths occurred in people who were notdiagnosed early while the cancer was curable. The 5-year survival fornoninvasive disease is about 88%, but only 50% for invasive disease,even without nodal involvement. The 5-year survival with metastasis tothe lymph nodes is only about 18%. This means that such patients willalmost certainly die from bladder cancer or metastases.

Bladder cancer develops by two routes--papillary and flat lesions.Approximately 15% of tumors progress from relatively noninvasivenonmetastatic lesions that are not life threatening to dangerousinvasive, metastatic lesions. Papillary lesions progress from benignpapillomas that protrude from the bladder surface, to noninvasivemalignancies growing into the bladder lumen, and finally to invasive,metastatic malignancies capable of causing death. At the lower grades ofthis progression, cells are cytologically "atypical," and appear similarto those seen in other conditions not related to cancer, notablyinflammation, obstruction, or stones. In the higher grades, such cellsare cytologically "suspicious" or "positive" and have a quitecharacteristic appearance. Flat lesions progress through several stagesof dysplasia (a premalignant lesion), culminating in carcinoma in situ(CIS), a noninvasive lesion in which the cells appear highly aberrantand are generally classified as "suspicious," even though the lesion isnot cancer per se. However, approximately one-third of such lesionsprogress to high-grade invasive cancer that is rapidly life threatening.The key to controlling bladder cancer is to detect lesions before theybecome invasive, and failing that, to detect invasive lesions as earlyas possible.

Until recently, bladder cancer was diagnosed almost exclusively byeither cystoscopy, wherein a fiber optic device is inserted into thebladder and lesions are detected visually by a urologist, or byconventional Papanicolaou staining of bladder cells obtained from urineor from a bladder wash (hereafter called "conventional cytology").Cystoscopy is an invasive procedure and is therefore unsuitable forscreening. Its major use is to detect tumors in patients expressing thesymptom complex characteristic of bladder cancer--hematuria, pain, orurinary obstruction. Unfortunately, symptoms usually do not occur untilthe tumor has progressed to a more dangerous grade or stage. Thedifficulty with conventional cytology is that while its sensitivity tohigh-grade lesions is approximately 90%, the sensitivity to lower gradelesions is highly dependent upon the training of the cytopathologist.Grade II and above tumors were detected with a sensitivity of 78% byhighly trained cytopathologists, but with much less efficiency bypathologists not specifically trained in urinary cytology. Highlycurable Grade I tumors are virtually undetectable by cytopathology.

Research has shown that an image analysis system can screen urinesamples having cells labeled with DNA-binding fluorescent dyes toidentify "alarms," which are potentially abnormal objects that exceedcertain size-brightness thresholds. When this is coupled with a trainedhuman observer to eliminate artifacts and visually classify cells andwith DNA measurements to detect cells that exceed the limit of 5C DNA,an effective cancer detection system results. Because the normal diploidamount of DNA (2C) can be doubled in dividing cells, it is not possibleto determine from ploidy alone whether a cell in the 2C-4C region is anormal cell in the process of division or an abnormal cell, additionalparameters are needed. Also, morphology alone is insufficient, sincemany low-grade tumors produce "atypical" cells which have minimallyaltered morphologies and are also produced by noncancer processes.

Malignant cells and high- and low-grade tumor cells with stained DNA canbe classified using appropriately modified criteria presented by L. G.Koss in "Tumors of the Urinary Tract and Prostate", in DiagnosticCytology and Its Histological Bases, Third ed. Vol. 2, J. B. Lippincott,Phil., pp. 749-811 (1979) which is hereby specifically incorporatedherein by reference.

The mainstay of cancer diagnosis has been the recognition of cancercells by a human expert. Humans can learn to recognize such cellsvisually, but the process of screening samples generally requires a highlevel of skill and knowledge. The work is generally fatiguing and boringdue to its repetitive nature. Cytology is therefore an excellentcandidate for automation, and it has been a desired goal to combinequantitative measurements of cell features to identify cancer cells incell specimens. Although a number of different approaches have beentried, the image analysis approach of attaching a television camera tothe microscope, and extracting "features" from the image, and then usingthose quantitative feature measurements as diagnostic parameters hasbeen used almost exclusively. The term "features" encompasses a widevariety of parameters, including dimensional and ratio parameters.Dimensional parameters include, but are not limited to, density(brightness or darkness), area and length measurements, and dispersionmeasurements (e.g., standard deviation) of features. Ratio parametersinclude nuclear/cytoplasmic area and other similar derived parameters.Image analysis is by and large an algorithmic approach, and ultimatelybogs down in the long computational times required to process images andfeatures with discriminant analysis or other statistical approaches.

Quantitative Fluorescence Image Analysis

Quantitative fluorescence image analysis (QFIA) is an instrumentationtechnology that can be used to quantitate molecular changes at thecellular level. The technology relies on a computerized microscopeprogrammed and standardized to automatically make biochemical andimmunochemical measurements at the molecular level in single cells usingfluorescent probes. The particular advantage of image analysis is thatquantitative molecular determinations can be directly correlated withthe wealth of information inherent in visual morphology. Properstandardization, and attention to the fluorescent and stoichiometricproperties of dyes are the key to using fluorescence as a quantitativemethodology.

Comparison of Integrated Grey Level (IGL) versus Optical Density (OD)and Quantification By Fluorescence versus Optical Density

Fluorescence and Absorption Probes

In order to detect certain molecules, it is generally necessary to use aprobe that specifically binds to the molecule of interest. With systemsthat depend upon measurement of light absorption, that probe is usuallyreferred to as a "stain". Relatively high concentrations are needed. Thestain can interact with the molecule of interest in two ways. The staincan cause a chemical reaction that leads to a colored or fluorescentproduct, or there is a physical interaction between the probe and themolecule of interest so that the probe is bound physically. The first isirreversible, that is the stain cannot be removed without some otherchemical reaction (e.g. bleaching). The second is reversible, that isthe stain can be washed out. At the concentrations of stains that areusually used, other substances will almost invariably also bind stain.

The chemistry of physically-binding absorption stains is not wellunderstood, and there is rarely a simple stoichiometry (the relationshipbetween the amount bound and the amount of molecule that binds thestain). In order to be able to see the stain, very high concentrationsmust be used. While it is true that the molecule of interest probablybinds the stain most strongly, and other substances usually bind moreweakly, the high concentrations involved force the binding equilibria ofthese weak binding substances strongly to the bound state. Thus, thepattern that is seen is a complex relationship involving the particularmolecules of interest and many other non-target molecules as well. Thenet result is that the amount of staining may bear only a very generalrelationship to the amount of the molecules of interest. This problem ismuch less severe when a chemical reaction (e.g. Feulgen reaction) isused rather than a physical interaction (e.g. hematoxylin and eosinstaining or Papanicolaou staining).

On the other hand, with fluorescence methods the higher contrast of thesignal (light on dark versus dark on light for absorption) means thatmeasurements are inherently much more sensitive and that the signal canbe detected at much lower probe concentrations. The stoichiometry isfrequently simple in that it is both proportional to the amount ofmolecule of interest and independent of the amount of probe once somelower limit that saturates the binding sites is exceeded. As a result,fluorescence methods are much more quantitatively accurate than methodsrelying on physical interactions.

Quantification by Optical Density

The Beer-Lambert law (Eq. 1) describes the relation between theabsorbance or optical density, OD (the two terms are usedinterchangeably); the intensity of transmitted light, I; the intensityof incident light (i.e. before passing through the absorbing substance),I_(o) ; the distance the light must pass through the absorbing object(pathlength), L; a molecular constant, a; and the concentration ofabsorbing molecules, c.

Absorption consists of a darker signal imposed upon a bright background.Operationally, I_(o) is measured by measuring the ##EQU1## intensity oflight transmitted through the slide in a region where there is noabsorbing sample while I is measured after passing through the sample.With an image analysis system, an image of a field is captured, andthose areas in the background where nothing is absorbing give ameasurement of I_(o). While absorbance and concentration are linearlyrelated, concentration and the intensity of transmitted light, I, arenot. Thus, a logarithmic transformation of data is required in order forresults to be accurate.

The amount of substance present is calculated by multiplying theconcentration by the volume, V, in which the absorbing material isconfined. In theory, the amount of DNA could be calculated from Eq. 2 ifthe molecular constant a were known and if there is a directproportionality between the amount of probe bound and the amount of DNAthat binds it. The actual image consists of a continuous range ofdifferent intensities because DNA is not evenly distributed within thenucleus. Note that the volume is the product of the cross sectionalarea, A, and the thickness, which is the same as L. Thus, thepathlength, L, disappears from the equations. ##EQU2##

A digitized image actually consists of discrete "pixels" or pictureelements. An example is the discrete dots that comprise an ordinarytelevision image. In digitization, each pixel, which actually representsan average over some small area, is assigned a discrete value, usuallybetween 0 and 256. White would be 0 while completely black would be 256.This value is referred to as the "grey level" and is denoted by thesymbol G. The net result is that the continuous variable I is replacedwith the discrete variable G. This operation lumps values that are veryclose to each other together in the same "box" or grey level value, butthe human eye is not able to distinguish the digitized signal from thecontinuous natural one. If the pixel area is S_(p), then the total DNAcontent of a cell nucleus is calculated by summing the DNA contained ineach volume corresponding to a pixel over all N pixels that comprise theimage. This volume is S_(p) L_(i), where L_(i) is the pathlength at theith pixel. An equation equivalent to Eq. 2 can be derived and is shownas Eq. 3. ##EQU3##

The summation term is the integrated optical density, or IOD. IOD istime consuming to measure because the logarithmic transformation must beperformed on each and every data point. Many systems abbreviate thecalculation and do not perform the logarithmic calculation on each pixelelement. Instead, they calculate an integrated grey level, IGL, which isthe average grey level of the image. ##EQU4## With absorbance, an errorfactor is created when the image is not of uniform density, as is thecase of images of cells. The error occurs because intensity oftransmitted light and concentration are logarithmically, not linearly,related. For an image analysis system, the more exact relation shown inEq. 3 is approximated as described in Eq. 5. The accuracy of theapproximation is dependent upon the range of variation in intensities.##EQU5##

In practice, a is not known and, indeed, varies from assay to assay andbatch to batch of samples because of the problem that the chemicalmethodology is not particularly reproducible. This problem occurswhether a chemical reaction such as the Feulgen procedure is used orstains such as the Papanicolaou stain are used. This problem ispartially overcome by using a standard material, for example, a celltype that has a DNA content that is assumed from other measurements. Inthe normal, resting cell, the amount of DNA is an exact, fixed amountthat is arbitrarily assigned a value of 2.0C. In practice, because, (1)some cells may be dividing (and have more DNA), (2) there are certainerrors inherent in measuring the OD of an image, and (3) there may besome cell-to-cell variation in staining or labeling, the DNA content ofa number of normal cells would be determined and the mode used to setthe point 2.0C. The mode is selected because it is relativelyinsensitive to individual variations in the cells being measured. If onefurther assumes that the molecular constant for the two cell types willbe identical (which is not always true), then the DNA content of anunknown cell type, DNA_(u), is related to the DNA content of thestandard cell, DNA_(s), by Eq. 6, where the term M(-logIGL_(s) /G₀,s)refers to the mode of the histogram of OD for the normal cells. ##EQU6##Quantification by Fluorescence

Fluorescence occurs when molecules absorb light, then dissipate some ofthe energy of the absorbed light in internal molecular transfers whereinlight is reemitted at a longer wavelength. In the absence offluorescence, when a molecule absorbs light, it rapidly emits the lightat the same wavelength. This cannot be distinguished from the light thatwas originally absorbed. In a microscope, fluorescence consists of abright signal on a dark background, which is the exact opposite ofabsorption. Additionally, the intensity of emitted light is directlyproportional to the number of molecules emitting light, and nologarithmic transformation is required. The fluorescence is alsodirectly proportional to the intensity of the exciting light, I_(e). Therelationship between the intensity of I_(e), the intensity offluorescence, I_(f), and concentration c of molecules is given by Eq. 7.##EQU7## The parameter K is a constant for a particular system. Itcontains a number of other variables including the quantum yield, whichis the fraction of light quanta that are emitted after absorption, thestrength of absorption of light at the exciting wavelength, andorientation factors peculiar to the particular instrumentalconfiguration. Generally, the molecules will be randomly oriented, whichmeans that light will be emitted at all orientations, and only somefraction will be picked up by the microscope objective.

The chemical selectivity of fluorescence dyes is generally much greaterthan that of absorption dyes. This occurs because in order to be able tosee staining of absorption dyes with a microscope, very highconcentrations of stain must be used because of the very shortpathlengths involved. At such high concentrations many molecules inaddition to those of interest will be stained, and very frequently theBeer-Lambert Law (Eq. 1) does not hold exactly, i.e., the relationshipbetween OD and concentration no longer is linear. Thus, some completelyarbitrary means of calibration must be used. In the case where Feulgenstaining is used as a means of calibration, staining is rarely exactlylinear, and the assumption that a cell which has four times the IGL of adiploid cell has four times the DNA content is rarely true.

In contrast to the situation with absorption, in the present invention,the relationship between fluorescence of the probe and the amount of DNAis independently established. How this process operates in the case ofDNA is explained thoroughly in the paper by McGowan, et al.("Equilibrium Binding of Hoechst 33258 and Hoechst 33342 Fluorochromeswith Rat Colorectal Cells", The Journal of Histochemistry andCytochemistry, Vol. 36, No. 7, 1988, pp. 757-762). The process isdescribed for immunologic probes in Jones, et al. ("QuantitativeImmunofluorescence, Anti-ras p21 Antibody Specificity, and CellularOncoprotein Levels", Biochemical and Biophysical ResearchCommunications, Vol. 167, No. 2, 1990, pp. 464-470). The references byMcGowan, et al., and by Jones, et al., are hereby incorporated herein byreference. Once it is established that this relationship holds, it isnot necessary to include a standard curve every time unless there isuncertainty that such a linear relationship holds.

In the present invention, the background of a fluorescent signal, whichis essentially black, is assigned a grey level of zero. The gain of thecamera is adjusted such that the usual cell images fall within somerange that allows for very bright signals. This arbitrary point isassigned to grey level 255. Thus, any signal brighter than this will betruncated at G=255. In the present invention, these truncationoccurrences are reported to prevent errors. The real advantage offluorescence is the linear relationship between fluorescence andconcentration using IGL leading to an essentially error-free measurementof amounts of molecules.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic of the overall process of cell analysis usingquantitative fluorescence image analysis.

FIG. 2A is a grey level image of an abnormal cell labeled with M344.

FIG. 2B is a grey level image of a normal cell labeled with M344.

FIG. 3 is a sensitivity/specificity plot of the M344 antibody in voidedurine cells.

FIG. 4 is a longitudinal follow up of a person using M344.

FIG. 5A is a plot of EGFR distribution in relation to field disease.

FIG. 5B is a plot of p185 distribution in relation to field disease.

FIG. 5C is a plot of G-actin distribution in relation to field disease.

FIG. 6 is a plot of p185 and G-actin quantities in relation to DNAploidy and field disease.

FIG. 7 is a plot of percentage of samples positive for 6 markers inrelation to field disease.

FIG. 8 is a specificity/sensitivity plot of abnormal DNA ploidy inrelation to smoking and benzidine exposure history.

FIG. 9 is a specificity/sensitivity plot of G-actin in relation tobenzidine exposure history.

FIG. 10A is a phase contrast photomicrograph of a urine sample which wasfixated with a non-crystal inhibiting fixative.

FIG. 10B is a phase contrast photomicrograph of the same urine sample asFIG. 10A except it was fixated using a crystallization-inhibiting means.

FIG. 11A is a phase contrast photomicrograph of a second urine samplewhich was fixated with a non-crystal inhibiting fixative.

FIG. 11B is a phase contrast photomicrograph of the same urine sample asFIG. 11A except it was fixated using a crystallization-inhibiting means.

FIG. 12A is a partial schematic of an Image Analysis System showing maincomponents.

FIG. 12B is a partial schematic of Image Analysis System showinginterface subunits.

FIG. 12C is a partial schematic of Image Analysis System showing output,image processor and image memory subunits.

FIG. 13 is a schematic of software-controlled quantitative fluorescenceimage analysis with G-actin and DNA as markers.

FIG. 14A is a schematic of the first stage of automated scan of slidewith image clipping using G-actin and DNA.

FIG. 14B is a schematic of the second stage of automated scan of slidewith image clipping using G-actin and DNA.

FIG. 15 is a schematic of software-controlled "rare-event" quantitativefluorescence image analysis using M344 and DNA.

FIG. 16A is a schematic of the first stage of "rare-event" scanning atlow magnification.

FIG. 16B is a schematic of the second stage of "rare-event" scanning athigh magnification.

FIG. 17 is a schematic of "rare-event" scanning and DNA quantitation athigh magnification using M344 and DNA.

FIG. 18 is a schematic of software-controlled quantitative fluorescenceimage analysis with DNA and two other markers.

FIG. 19A is a schematic of the first stage of triple label scan.

FIG. 19B is a schematic of the second stage of triple label scan.

FIG. 20 is a graph of fluorescent excitation and emission patterns ofTexas Red for correcting for autofluorescence.

FIG. 21 is a specificity/sensitivity plot of the M344 antibody in cellsof bladder washes.

DESCRIPTION

The present invention provides a system for evaluating one or morecytological markers for cell analysis, particularly for individualcancer risk assessment, cancer diagnosis, and for monitoring therapeuticeffectiveness and cancer recurrence. Quantitative measurements ofphenotypic marker profiles can be used to document the risk ofmalignancy faced by an individual. While many genetic changes may leadto the malignant phenotype, a much smaller number of phenotypic markersmay be used to chart the progress towards malignancy. The currentinvention represents in one version the first successful application ofthe neural network approach where the input is a gray level imagederived from cells labeled for specific molecules using fluorescentprobes.

The present invention is generally described in the schematic diagramshown in FIG. 1 and can be briefly summarized as follows. A sample ofbiological cells is collected. The cells may be collected by washing anorgan, such as a bladder, a colon, a small intestine, or bronchialtissues, for example. Or the cells may be collected from body fluid suchas urine, pleural fluid or sputum, for example. Or the cells may becollected from a needle aspiration of a body part, gland, or organ, forexample the prostate gland.

The sample is prepared for application to a slide, includingoptimization of the number of cells on the slide. The cells are appliedto the slide in preparation for quantitative labeling. The slide issequentially processed wherein a series of one or more quantitativefluorescent labels is applied to the cell sample in such a way that thelabels do not interfere with their affinities to specific cytologicalmarkers. In the preferred embodiment of the present invention thefluorescent label is comprised of a fluorochrome bound to an affinityprobe. By "fluorochrome bound to an affinity probe" is meant anyfluorochrome which is attached directly or indirectly to an affinityprobe, or any fluorochrome which itself acts as an affinity probe. Anexample of the latter is Hoechst 33258. Several fluorochromes are notedin Table IV.

The term affinity probe as used herein is defined to include a materialhaving a specific affinity for a particular type of cytological markerand may include, but is not limited to antibodies, peptides orpolypeptides, nucleotides or polynucleotides, dyes, carbohydrates,lectins, and other ligands, and combinations thereof. Several examplesof affinity probes are the M344 antibody, anti-EGFR probes such as AB-1,anti-HER-2/neu protein probes such as TA1, and DNase I.

The slide is analyzed using a quantitative fluorescence image analysissystem using a system including a microscope means which automaticallyselects and stores the grey level images of from about 24 to 115 cellsper slide. The term microscope means, as used herein, refers to anymeans by which cells may be magnified to be viewed at a microscopiclevel, and may include any viewing means which allows the quantitativemeasurement of cellular markers within a cell. For example, from a slidedouble-labeled for actin and DNA, 64 images may be stored for DNAevaluation and 48 images may be stored for actin evaluation. The termactin, as used herein, means any cellular actin-type molecule such asF-actin, G-actin, and any other cytological or nuclear actins. Each cellimage may be corrected for extraneous fluorescence including backgroundfluorescence (from the sample medium) and autofluorescence. Cells arethen quantitatively analyzed for fluorescence of the specificcytological markers. The term cytological marker is meant herein as anycytological feature which may serve to "mark" a particular type of cellor other component of the cell sample and may include, but is notlimited to, tumor associated antigens, receptors, cytoskeletal proteins,oncogene proteins, DNA (including genes and chromosomes) and RNA andwhich may be labeled by a fluorescent label.

Cells which require further confirmation or documentation as normal orabnormal are then evaluated either by a trained technician or by atrained neural net computer. For example, certain cells whichdemonstrate a high quantity of the M344 label of the p300 marker typicalof an abnormal cell (i.e., the cells are brightly positive) may actuallybe normal. These abnormal (cancerous) cells can be distinguished fromnormal cells because of the differences in the pattern of fluorescencewithin the cell (FIGS. 2A and 2B). Such differences in fluorescencepatterns can be distinguished by a human operator as well as by atrained neural net computer although the neural net computer performsthe task more quickly. Such a finding, where humans can easily recognizethe difference but find it difficult to encode into rules represents anideal use of neural networks. Other markers may occur in similarlydifferentiable patterns in normal and abnormal cells and can thereby bedistinguishable. Once the markers have been analyzed, the markerprofile, which contains information about the quantities of the markersin the cells as well as how many cells may be positive for certainmarkers, the cell sample may be further classified.

In the present invention, the step of classifying the cell sample maycomprise (1) generating a marker profile for the cell sample, (2)assigning a cancer risk level to the person who contributed the cellsample, (3) generating a proposed course of clinical action to befollowed by the person from whom the cell sample was obtained, or (4)any combination of (1), (2) and (3).

Normal cells within the cell sample are used to establish thecalibration for diploid DNA. For example, even in urine from a personhaving a bladder tumor, most of the cells are normal. For urine cells,approximately 100-200 such cells are measured and the fluorescenceintensities are plotted. The mode, the most probable value, of thedistribution is determined. Since DNA normally has a discrete value,this fluorescence corresponds to 2C, or the diploid amount of DNA. Theadvantage of the mode is that the inadvertent inclusion of a fewabnormal cells in the distribution does not affect the mode. The DNAcontent of an unknown cell is determined from Eq. 8, where M(IGL_(N))represents the mode fluorescence of the normal cells and B is thebackground fluorescence, which is usually near zero. ##EQU8## Incontrast to Eq. 6, Eq. 8 is simpler with less inherent error.

Understanding the concept of risk is essential to any program of cancercontrol. The appearance of a clinically detectable tumor is the endpoint of a long process that has occurred over many years. It isanalogous to a heart attack, which is the end point of a long process ofnarrowing of the arteries. Both processes are detectable, and markerscan point to risk. In the case of heart attacks, elevated cholesterol issuch a marker and is often used as an indicator of elevated risk and theneed for treatment.

The present invention offers an automated approach using QFIA formultiple marker measurements at the cellular level. By detecting fielddisease as well as tumors, such measurements can stratify groups labeledas high risk by epidemiologic risk factors. Screening for positivemarkers in exfoliated cells is noninvasive, and interventions can betargeted to individuals with objective indicators of abnormality.Additionally, marker profiles can be used to target aggressive therapiesat individuals with markers indicating high risk for progression ormetastasis while indicating conservative therapy for individualsidentified as being at lower risk. A third application lies inmonitoring cancer patients for response to therapy and for recurrence.The degree of ablation of abnormal markers by therapy will undoubtedlycorrelate with risk of recurrence, and observing progressive developmentof abnormal markers may be used to signal early intervention or diminishthe need for invasive cystoscopy as a monitoring tool when it does notoccur.

Changes in cellular differentiation and proliferation are results of thecarcinogenic process. These changes are not necessarily linked to eachother. For example, a cytologic low-grade-appearing tumor may have ahigh proliferation rate with well-differentiated cellular architecture.F-actin is a quantitative marker that reflects the degree ofdifferentiation in model cell-culture systems, with low levelsreflecting a less differentiated state.

As shown below for bladder cancer, because quantitative differences inexpression of certain markers are important characteristicsdistinguishing field, low, and high grade tumor cells, marker profilesbased upon quantitative methods, such as QFIA, are more likely to beuseful than qualitative or semi-quantitative methods.

Bladder Cancer Tumorigenesis

The biochemical changes produced by the process of tumorigenesis can bedetected prior to the development of a cancer, and by evaluating thosechanges using specific, quantitative markers, an individual's risk fordeveloping bladder cancer and, more specifically, dangerous, invasivebladder cancer, can be assessed. For example, while almost all cancercells contain abnormally low amounts of F-actin, so do many cells fromdysplasias. Just as not all people having elevated cholesterol ornarrowing of the arteries suffer heart attacks, not all dysplasiasdevelop into cancers. However, no cancer appears to develop that doesnot proceed through such a stage. Thus, the finding of cells withdecreased F-actin, or increased G-actin, its precursor, is a marker forrisk.

More information can be obtained by measuring additional markers, suchas tumor related antigens or DNA ploidy. In a study based upon auniversity referral population, which is weighted toward recurrentcancers, the combination of DNA ploidy and visual classification ofcells by a trained human observer had an approximate 95% specificity(fraction of noncancer samples not called abnormal), a virtual 100%sensitivity to high-grade tumors, an approximate 80% sensitivity tolow-grade tumors and a 20%-30% advantage over conventional Papanicolaoucytology.

Smoking itself can produce cells with >5C DNA by inducing failed celldivisions, although the majority of smokers with cells with >5C in theirurine do not develop cancer. A small percentage of smokers will havepositive results for this marker when judged against a threshold derivedfrom a non-smoking population. However, the finding of both abnormalactin and DNA ploidy is a strong indicator of risk in a smoker. Theabnormal actin marker points to the process of altered cellulardifferentiation and suggests the cells with abnormal DNA ploidy arosefrom a dysplastic lesion rather than the process described above. Whilefailed cell divisions are abnormal and indicate some risk for cancer,the presence of dysplastic lesions is indicative of much higher risk forcancer. Addition of a third marker can provide even more information.

For example, presence of the p300 protein produced by low-grade tumorcells and some dysplastic cells (and detected by the M344 antibody), isa further indicator of risk for cancer. In a study of over 600 urinesamples from workers exposed to chemicals that cause bladder cancer,some of whom also smoked, some 15% showed abnormal actin, some 8% showedabnormal DNA and some 2% showed abnormal p300. However, only 5 showedall 3 markers abnormal, and 3 of these were found to have bladdercancer. Of these, 2 had been positive a year earlier, but had not had adetectable cancer at that time. Thus, having all 3 markers positiveappears to occur late in the tumorigenesis, near the time when a tumorcan be detected, and is a very strong indicator of risk.

Bladder cancer is frequently multifocal, and large areas may displayaltered biochemical or morphological changes ("field disease")indicative of increased risk. These findings are particularly importantto occupational studies because, as noted above, F-actin may be a usefulmarker for individual bladder cancer risk assignment. More importantly,such early transformation-related events might be correctable byretinoids, providing a theoretical approach for chemopreventiveintervention. Based upon findings that retinoids administered in animalcarcinogenesis models reverse cytologic changes detectable byquantitative fluorescence imaging analysis (QFIA), retinoidchemoprevention in persons identified with QFIA as being at high riskfor cancer may well offer a more effective control strategy than waitinguntil tumors appear to administer retinoids. Although DNA ploidy changescan occur early in experimental carcinogenesis, F-actin or other markersmay be as or more effective intermediate end-point indicators.

The results to date indicate that QFIA measurements on voided urinecells can play an important role in occupational screening programs. Thesensitivity and specificity of currently available QFIA technologycombining DNA and morphology are better than is usual in most screeningtests. The main shortcomings are the high cost and the inability ofcurrently available instrumentation to make multiple marker measurementsthat could enhance sensitivity to low-grade tumors and simultaneouslyprovide an individual risk profile. The objective of the presentinvention is to overcome these shortcomings.

Specificity-Sensitivity of the p300 Marker

The sensitivity and specificity of the p300-marker was independentlytested in a study of symptomatic and asymptomatic subjects and patientswith bladder cancer using the M344 antibody. The specificity usingvoided urines was determined with a mixture of samples, consisting ofcompletely normal, asymptomatic controls that approximately matched theage distribution of cancer cases and patients currently without cancerbeing monitored for recurrence. Preliminary data analysis has shown thatp300 is a valuable marker for the detection of low-grade bladdercancers, with the amount per cell being less significant in identifyingtumor cells than the appearance of a specific pattern consisting ofsmall, relatively bright granular fluorescence in the cytoplasm (FIG.2A) versus a less granularly, more generally fluorescent appearance innormal cells (FIG. 2B).

Analysis has shown that cells can be effectively scored as either"positive" or "negative". Additional studies have shown that in anygiven tumor, not only are not all cells positive for the marker, butfrequently the positive fraction was small. This suggested that thefinding of a small number of positive cells (i.e., "rare events") in aurine sample might be significant. In order to determine the optimalthreshold, the data were analyzed by the "receiver operatingcharacteristics" (ROC) curve method in which sensitivity and specificityare plotted as a function of the threshold selected. FIG. 3 shows theM344 ROC curve for voided urines.

FIG. 3 indicates that finding two cells per 10,000 cells examined in avoided urine is a strong, positive marker for cancer risk. The p300protein is also expressed by at least some premalignant lesions, asshown by the finding of positive cells in some of the controls. Positivecells were also found in patients with benign prostatic hyperplasia andbladder outlet obstruction, suggesting the protein is a marker foraltered differentiation that can be produced either by carcinogenesis orby the promoting effects of urinary stasis. Completely asymptomaticnormals express this protein very rarely, and the specificity is inexcess of 98% with such individuals. Overall, with the inclusion ofsymptomatic individuals, the sensitivity with voided urines was 90% with90% specificity. Preliminary analysis of the data using the stratifiedrisk approach and the field disease studies suggest that p300 appearsearlier than abnormal DNA ploidy but after abnormal G-actin.

Preliminary analysis of data from those individuals from whom at leastone year follow up is available strongly supports the use of p300 as auseful marker for monitoring response to therapy and risk of recurrence.To this point, no patient who normalized (became negative for p300)experienced a recurrence of cancer without prior reappearance ofM344-positive cells. Also, patients who did not normalize in response totreatment have tended to recur rapidly and at a rate much in excess ofthe recurrence rate by patients who normalize. FIG. 4 shows thelong-term follow up of a subject treated with BCG. The subject oninitial diagnosis was found to have several, large low-grade tumorswhich were resected. The subject was treated with BCG, as indicated bythe boxes below the graph, and the number of positive cells dropped tothe normal range. Later, positive cells appeared and the patientexperienced a recurrence. After resection and treatment with anadditional course of BCG, the patient has not recurred and his resultshave remained negative.

G-actin as an Early Marker for Bladder Cancer Risk

G-actin has also been investigated as a marker in cancer patients.Globular actin or G-actin, as the monomeric precursor for F-actin, bearsa reciprocal relationship to F-actin. Decreased F-actin (a cytoskeletalprotein) levels in urinary tract cells is a marker for decreaseddifferentiation. In 9 patients with biopsy-proven disease the meanG-actin content of urinary cells was 105 units, and in 19 asymptomaticcontrols was 48.4 units. This difference was significant at p<0.001.

EXAMPLE 1 Quantitative Fluorescence Image Analysis of Cells withMultiple Markers

Transitional cell carcinomas (TCC) of the bladder are known tofrequently develop as multiple foci in time and place within thebladder. The bladder represents a complex ecosystem of interfacingepithelial and stromal cells, and the progressive subversion of growthand differentiation controls, leading to eventual emergence of cellscapable of at least partial autonomous growth, requires years. Alteredhistopathology is a relatively late event in carcinogenesis, butbiochemical or genetic manifestations of carcinogenic damage may bedetectable years earlier. As described below, phenotypic markers weremapped in the bladder by quantifying markers in sample specimens fromthe tumor, the adjacent epithelium and from distant epithelium inpersons with bladder cancer and in bladder cells from normal persons.Because quantitative rather than qualitative differences in geneexpression and protein levels probably underlay most of the differencesbetween malignant and normal phenotypes, the ability to quantify markersis needed. Accordingly, QFIA was used to quantify the phenotypic markersin single cells from the sample specimens. Because of its visualmorphologic component, and in contrast to methods such as flowcytometry, QFIA can link conventional morphologic assessment withquantitative biochemical markers at the single cell level. The markersincluded; QFIA cytology, a combination of visual morphology and thepresence of cells with DNA in excess of 5C (which is a marker forgenetic instability), the p300 tumor-related antigen detected by theM344 monoclonal antibody, the differentiation-related proteins epidermalgrowth factor receptor (EGFR) and G-actin, and p185, a protein productof the HER-2/neu oncogene.

Experimental Methodology

The patient population consisted of 30 patients with TCC and 6 noncancercontrols, all of whom gave informed consent. To obtain single cells forQFIA analysis, "touch preps" were made from biopsy specimens of tumor,adjacent field and random distant epithelium in the operating room. Thesurface of the tissue was touched to a polylysine-coated slide, and theremainder of the tissue was submitted for routine pathology. Separateforceps were used for each piece of tissue to prevent crosscontamination. The slides were then triple-labeled for DNA using 8 μMHoechst 33258, for p185 using the TA1 antibody directly conjugated toTexas Red, and for p300 using the M344 mouse monoclonal antibody and a3-stage sequence using biotin goat antimouse secondary antibody andBodipy-labeled avidin. With fourteen of the tumors and all six controlsa second triple-labeled slide was prepared labeling for DNA as above,for EGFR using AB-l mouse monoclonal (Oncogene Science) and the samevisualization system as was used with M344 above, and for DNase I(Molecular Probes) directly conjugated to Texas Red to detect G-actin. Acorresponding negative control slide omitting primary antibodies wasalso prepared. All reagents were optimized to achieve saturation.Labeling was carried out in an automated slide labeling device(Instrumentation Laboratories Code-On). With each batch of slides,standard cell lines known to be high and low expressors for eachquantitative marker were also included, thereby enabling thequantitation of each marker found to be present in a particular cell.Tables I-III show examples of three different staining sequences similarto the staining procedure used in Example 1.

                  TABLE I                                                         ______________________________________                                        Stain Sequence - G-actin + M344 + DNA                                         EVENT STATION  TIME        SOLUTION                                           ______________________________________                                        1     11       1.00    MIX   PAD                                              2     14       1.00    MIX   M344                                             3     7        30.00         INCUBATOR                                        4     8        0.50          PAD                                              5     10       0.10          1X AUTO. BUFFER (BM-M30)                         6     9        0.50          PAD                                              7     10       0.10          1X AUTO. BUFFER (BM-M30)                         8     11       0.50          PAD                                              9     10       0.50          1X AUTO. BUFFER (BM-M30)                         10    12       1.00    MIX   PAD                                              11    15       1.00    MIX   SECONDARY                                        12    7        30.00         INCUBATOR                                        13    8        0.50          PAD                                              14    10       0.10          1X AUTO. BUFFER (BM-M30)                         15    9        0.50          PAD                                              16    10       0.10          1X AUTO. BUFFER (BM-M30)                         17    11       0.50          PAD                                              18    10       0.10          1X AUTO. BUFFER (BM-M30)                         19    12       0.50          PAD                                              20    10       0.10          1X AUTO. BUFFER (BM-M30)                         21    11       0.50          PAD                                              22    10       0.10          1X AUTO. BUFFER (BM-M30)                         23    12       1.00    MIX   PAD                                              24    16       1.00    MIX   BODIPY                                           25    7        30.00         INCUBATOR                                        26    11       0.50          PAD                                              27    10       0.10          1X AUTO. BUFFER (BM-M30)                         28    12       0.50          PAD                                              29    10       0.10          1X AUTO. BUFFER (BM-M30)                         30    9        1.00          PAD                                              31    10       0.60          1X AUTO. BUFFER (BM-M30)                         32    8        1.00    MIX   PAD                                              33    17       1.00    MIX   G-ACTIN                                          34    7        30.00         INCUBATOR                                        35    8        1.00          PAD                                              36    10       0.10          1X AUTO. BUFFER (BM-M30)                         37    11       0.50          PAD                                              38    10       0.10          1X AUTO. BUFFER (BM-M30)                         39    11       0.60          PAD                                              40    10       0.10          1X AUTO. BUFFER (BM-M30)                         41    8        1.00    MIX   PAD                                              42    6        2.00          HOECHST                                          43    12       0.30          PAD                                              44    6        0.50          HOECHST                                          45    11       0.30          PAD                                              46    6        0.50          HOECHST                                          47    9        0.30          PAD                                              48    6        0.50          HOECHST                                          49    8        0.30          PAD                                              50    6        2.00          HOECHST                                          The total processing time: 146.10 min.                                        ______________________________________                                    

                  TABLE II                                                        ______________________________________                                        Stain Sequence - M344 + DNA                                                   EVENT  STATION  TIME      SOLUTION                                            ______________________________________                                        1      11       1.00   MIX  PAD                                               2      15       1.00   MIX  PRIMARY ANTIBODY                                  3      7        30.00       HEATED WET CHAMBER                                4      8        0.50        PAD                                               5      10       0.10        1X AUTO. BUFFER (BM-M30)                          6      9        0.30        PAD                                               7      10       0.10        1X AUTO. BUFFER (BM-M30)                          8      11       0.30        PAD                                               9      10       0.50        1X AUTO. BUFFER (BM-M30)                          10     12       1.00   MIX  PAD                                               11     16       1.00   MIX  BIOTINYLATED SECONDARY                            12     7        30.00       INCUBATOR                                         13     8        0.50        PAD                                               14     10       0.10        1X AUTO. BUFFER (BM-M30)                          15     9        0.50        PAD                                               16     10       0.10        1X AUTO. BUFFER (BM-M30)                          17     11       0.50        PAD                                               18     10       0.10        1X AUTO. BUFFER (BM-M30)                          19     12       0.50   MIX  PAD                                               20     17       0.50   MIX  TEXAS RED                                         21     7        30.00       INCUBATOR                                         22     8        0.50        PAD                                               23     10       0.50        1X AUTO. BUFFER (BM-M30)                          24     9        0.50        PAD                                               25     10       0.50        1X AUTO. BUFFER (BM-M30)                          26     11       0.50        PAD                                               27     10       0.50        1X AUTO. BUFFER (BM-M30)                          28     12       0.50        PAD                                               29     10       0.50        1X AUTO. BUFFER (BM-M30)                          30     9        1.00        PAD                                               31     10       0.60        1X AUTO. BUFFER (BM-M30)                          32     12       1.00   MIX  PAD                                               33     6        2.00        HOECHST                                           34     11       0.60        PAD                                               35     6        0.50        HOECHST                                           36     12       0.60        PAD                                               37     6        0.50        HOECHST                                           38     11       0.60        PAD                                               39     6        0.50        HOECHST                                           40     12       0.60        PAD                                               41     6        0.50        HOECHST                                           42     11       0.50        PAD                                               43     6        2.00        HOECHST                                           44     12       0.60        PAD                                               45     10       0.50        1X AUTO. BUFFER (BM-M30)                          The total processing time: 115.20 min.                                        ______________________________________                                    

                  TABLE III                                                       ______________________________________                                        Stain Sequence - F-Actin + DNA                                                EVENT STATION  TIME        SOLUTION                                           ______________________________________                                        1     8        100     MIX   PAD                                              2     17       1.00    MIX   ANTIBODY                                         3     7        30.00         INCUBATOR                                        4     8        1.00          PAD                                              5     10       0.10          1X AUTO. BUFFER (BM-M30)                         6     11       0.50          PAD                                              7     10       0.10          1X AUTO. BUFFER (BM-M30)                         8     11       0.60          PAD                                              9     10       0.10          1X AUTO. BUFFER (BM-M30)                         10    8        1.00    MIX   PAD                                              11    6        2.00          HOECHST                                          12    12       0.30          PAD                                              13    6        0.50          HOECHST                                          14    11       0.30          PAD                                              15    6        0.50          HOECHST                                          16    9        0.30          PAD                                              17    6        0.50          HOECHST                                          18    8        0.30          PAD                                              19    6        2.00          HOECHST                                          The total processing time: 42.10 min.                                         ______________________________________                                    

Filter Sets Used for Quantitation of Fluorochromes

Filters are image analysis quality (DRLP grade) with high precision inangle of incidence to avoid problems with image registration. A/R coatedrear surface dichroics eliminate additional undesired stray light forquantitative purposes. Narrow band emission filters are selected tomaximize fluorochrome properties and minimize non-specificautofluorescence (see Table IV).

                  TABLE IV                                                        ______________________________________                                                             Dichroic                                                 Fluorochrome                                                                            Excitation Beam Splitter                                                                              Emission                                    ______________________________________                                        Hoechst 33258                                                                           360 ± 50 nm                                                                           400 @ 45° DRLP                                                                      450 ± 50 nm                              Texas Red 560 ± 40 nm                                                                           595 @ 45° DRLP                                                                      630 ± 23 nm                              Bodipy-Fitc                                                                             485 ± 22 nm                                                                           505 @ 45° DRLP                                                                      530 ± 30 nm                              Autofluorescence                                                                        540 ± 23 nm                                                                           565 @ 45° DRLP                                                                      590 + 35 nm                                 ______________________________________                                    

Neutral Density Filters

Neutral Density filters are comprised of quartz and have a clean flatuniformly coated surface. The degree of transmission required isdetermined by the fluorochrome, sensitivity of the imaging camera, andthe specific marker quantified. A typical set of seven filters wouldinclude the following properties at 485 nm absorption (see Table V). Thelinearity of the camera system can be quantified using knowntransmission values at each given wavelength. A reference table is thenused to correct for non-linearity at each wavelength.

                  TABLE V                                                         ______________________________________                                        % Transmission Optical Density                                                ______________________________________                                        79.4           0.1                                                            50.1           0.3                                                            25.1           0.6                                                            10.0           1.0                                                            1.0            2.0                                                            0.3            2.5                                                            0.1            3.0                                                            ______________________________________                                    

The present invention also contemplates using a first fluorochrome and asecond fluorochrome which may be excited by a single excitationwavelength. In such a case, the single excitation wavelength whichcauses a first fluorochrome to emit fluorescent light at a firstemission wavelength, may also be effective in causing a secondfluorochrome to emit fluorescent light at a second emission wavelengthwhich is different from the first emission wavelength.

Figure Legends

FIGS. 5A-C: Cumulative distributions of EGFR (FIG. 5A), p185 (FIG. 5B)and G-actin (FIG. 5C) as measured by IGL (integrated grey level), LogIGL! and AGL (average grey level), respectively. IGL and AGL arecalculated by the IBAS image analysis system (Zeiss Instruments) fromthe digitized, grey-level fluorescence images and represent thefluorescence intensity integrated over a cell image (IGL) and theaverage intensity (AGL) of all pixels comprising the image. Markers werequantified using the IBAS on a cell by cell basis from 50-100 cells perslide. In tumor biopsies, regions of tumor cells were specificallyanalyzed. In nontumor specimens, cells were randomly selected. In bothcases infiltrating lymphocytes or blood cells were specificallyexcluded. The immunofluorescence assays were calibrated against celllines known to express high and low amounts of the proteins. The 18-3-7line transfected with an expression vector containing HER-2/neu and theA431 line served as positive controls for p185 and EGFR respectively. Alarge number of slides were prepared from a single batch of each cellline. A positive (with primary antibody) and negative (without primaryantibody) slide for each marker was included with each batch of patientsamples, and the cells were labeled and analyzed as described. The IGLor AGL was corrected for background fluorescence by subtracting the meanIGL and AGL determined from approximately 100 cells on the negativecontrol slide. -□-=control; --=field of low grade TCC; -▾-=low gradeTCC; -▪-=field of high grade TCC, and -*-=high grade TCC.

FIG. 6: Relationships between DNA ploidy and log p185! or G-actin as afunction of biopsy site. The cells were stratified by DNA ploidy and themean marker content for all the cells from all the patients were plottedas a function of ploidy as shown on the figure.

FIG. 7: The progression of biochemical markers from control (n=6),random distant field (n=27), adjacent field (n=24) and tumor (n=30) ofTCC with markers scored by positive/negative criteria. Cytology wasscored by a trained cytologist and confirmed by a pathologist. Thepresence of cells with "suspicious" morphology labeled the sample as"positive" as did the presence of any cells with >5C DNA. The p300marker (M344) was scored visually by two independent observers. A samplewas considered positive if any positive cells were noted. EGFR andG-actin were approximately normally distributed while p185 (Neu) showeda lognormal distribution. For p185, the mean IGL of cells on thenegative control slide was calculated and was subtracted from the IGL ofeach cell on the sample slide. If the adjusted mean IGL significantly(t-test) exceeded the adjusted IGL of a low expressing cell line(3T3SW480), the sample was labeled as positive. For EGFR, a histogram ofnormal cells from control patients was constructed, and the presence ofcells above the upper limit of normal (threshold, FIG. 3) was used as anindicator of positive. For G-actin, a sample was labeled as positive ifthe mean IGL was significantly higher (p<0.05 by Student's t-test) thanthe mean of the control patients.

Results

FIGS. 5A-C present the cumulative frequency distributions for EGFR, p185and G-actin. EGFR and G-actin showed distributions markedly skewed fromnormal while p185 was distributed normally. The progression in markerscould be demonstrated in two ways from these plots by eitherestablishing a threshold or by comparing mean values. EGFR shiftedprogressively to higher expression levels that could be exploited todevelop a positive-negative schema in terms of the fraction of cellsexceeding the threshold for normal cells (Threshold 1). The field fromlow-grade tumors showed a significant increase over the normal field incells expressing 2-5 EGFR units. Almost identical distribution of EGFRexpression by low-grade tumors and the field from high-grade tumors wasobserved, but high-grade tumors expressed significantly larger numbersof high expressing cells (>5 EGFR units). Little if any p185 expressionwas found in the normal controls, but in all the abnormal samples anapproximately normal distribution was seen with a progressive shift inthe maximum to higher mean expression from low grade to high grade andfrom field to tumor. A schema based upon comparison of mean values wasderived in order to score samples. With G-actin all the abnormal cellsclustered at mean values that were 3-5 times the mean value of normalcells. A single threshold separated positive from negative while themean values displayed progression.

The markers were investigated for clustering and statisticalindependence. Cluster analysis using SAS showed the variables fell intothree relatively independent groups. G-actin and EGFR were in onecluster while cells with more than 5C DNA and p185 were in a secondcluster. Visual morphology, being a qualitative variable not amenable tocluster analysis, was placed into the second cluster because of theclose association with DNA (P<0.05 by Cochran-Mantel-Haenszel analysis).The p300 marker was the third independent cluster.

The measurement of multiple parameters on the same cells can be used todelineate markers that are coexpressed in the same cells and those thatare expressed in different cells or which are unrelated to each other.FIG. 6 shows the distribution of p185 and G-actin as a function of theDNA content of the cell stratified by biopsy sites. The associationbetween p185 and DNA content and the independence of G-actin on DNAcontent are both clearly evident. The pattern for EGFR was almostidentical to that of p185 and was not shown.

Differences in marker expression, particularly in EGFR, were observedbetween low and high grade tumors and their fields. When a thresholdof >5% of cells with more than 5IGL EGFR content (Threshold 2 in FIG.5A) was used, 2/8 low grade tumors and the corresponding fields werepositive, while 6/6 high grade tumor and 3/6 high grade field sampleswere positive. This difference was statistically different at p<0.05 bythe Mann-Whitney U-test. With visual DNA morphology, the differencebetween low and high grade tumors was statistically significant byChi-square at p<0.05. Only 24% (4/17) of fields adjacent to low gradetumors had positive visual DNA morphology while in the fields adjacentto high grade tumors, 57% (4/7) were positive (p=0.1 by Chi-square).With p300, the decrease of percentage positive in adjacent field fromlow grade to high grade tumors (14/17 vs 2/7) was statisticallydifferent (p<0.05 by Chi-square). For p185, the positive-negativeclassification was not significantly different for low and high gradesamples, but the mean values of the distribution of IGL between high andlow grade tumors (FIG. 5B) was significantly different by analysis ofvariance (p=0.04).

FIG. 7 summarizes the results after each parameter was stratified into abinary classification schema as described in the legend. FIG. 7 presentsthe percent of cases positive for each marker in the distant field,adjacent field and tumor without respect to tumor grade and ofcorresponding tissue from control bladders. Each marker showed aprogression from distant field to adjacent field to tumor tissue. Withthe exception of p300 for which one of the six controls was positive,none of the markers were positive in the control bladders. Every markerwas positive in some fraction of the distant field biopsies, and a clearprogression in markers was evident in the adjacent field biopsies.G-actin was positive in virtually every tumor and in 58% of distant and73% of adjacent field biopsies. A large increase between distant andadjacent field was observed for both p300 and DNA ploidy (p<0.01 and0.05, respectively, by Chi-square).

The data presented in this example of phenotypic markers verify theconcept of "field disease" at the biochemical level and which heretoforehad been defined solely in histopathologic terms. Clearly, differencesin some biochemical phenotypic markers are manifested well before anabnormal histopathology is evident, and some are even abnormallyelevated in distant biopsy sites. DNA ploidy changes as far as 10 cmfrom a primary colon tumor have been observed. The quantitative changesof markers displayed a sequential and progressive pattern from normal,random distant site, adjacent field, and tumor which mapped theprogressive course of bladder carcinogenesis in terms of quantitativechanges in specific molecules and other phenotypic markers.

These results indicate that the differentiation-related cytoskeletalprotein G-actin seems to reflect very early events in bladdercarcinogenesis, being abnormal in 60% of the distant biopsies frombladders that contain tumors and essentially 100% of tumors themselves.Previous findings of decreased F-actin in bladder cancer and a strongrelation to bladder cancer risk suggested that elevated G-actin (whichis the monomeric precursor of F-actin) should be observed. The datapresented here confirm the concept that alteration in the cytoskeletalreflected by a shift from a high level of microfilament actin (F-actin)to a high level of globular actin (G-actin) represents an early, commonmarker for dedifferentiation and shows this phenotypic change persistsduring cytologic dedifferentiation.

The p300 marker, which is apparently not normally expressed inurothelium, is preferentially expressed in low grade, diploid tumorcells, some premalignant lesions and high grade tumor cells, as well asin a high fraction of fields adjacent to low grade tumors. Previousfindings suggesting that abnormal p300 expression was associated withabnormal F-actin but not with abnormal ploidy suggests this marker seemsto represent early phenotypic changes that may be related todifferentiation.

EGFR clustered with G-actin, even though some correlation with thechanges in DNA ploidy, p185 and morphology was noted, which indicatesEGFR reflects a set of phenotypic changes more reflective of alterationsin differentiation program than of factors relating to the othermarkers. The enhanced EGFR levels of high-grade tumor cells probablyresults from a loss of EGF-mediated down-regulation of EGFR expressionby high grade tumor cells, a mechanism which functions in normalurothelium and low-grade tumor cells. The quantitative difference inp185 expression between low and high grade tumors suggests thepossibility that abnormal HER-2/neu expression in bladder cancers mayindicate an elevated risk for progression from low to high gradedisease. Elevated p185 can be either a primary event, reflectingactivation of the HER-2/neu oncogene, or it may be a phenotypic markerfor other genetic events leading to upregulation of HER-2/neu.

Quantitative differences in expression of p185 and EGFR, and not thefraction of positive cells, were both shown to be importantcharacteristics distinguishing field, low, and high grade tumor cells.Therefore, marker profiles based upon QFIA are more likely to be usefulthan immunohistochemistry, which is semi-quantitative at best.

These results clearly demonstrate that marker-positive cells canoriginate in areas of the bladder other than tumor, and could representeither premalignant cells or the result of altering the cytokineenvironment of the bladder with consequent changes in the growthcharacteristics of the bladder epithelium. These changes denote aphenotype that is strongly associated with the cancer process and whichcan be used to monitor response to therapy and predict recurrence aswell as to detect disease.

Research in evaluating and validating markers has been slowed by thenecessity to perform a 5-year follow up study on every marker. Analternative evaluation method that may rapidly provide valuableinformation concerning how a given marker relates to existing markersand whether it occurs early or late can be obtained by using subjectsstratified according to risk, or according to the probability that theywill eventually develop disease. The highest risk group would includepatients with histologically-proven disease. The next highest riskcategory would be patients with a proven history of disease and withabnormal DNA ploidy and cytology, but currently undetectable for thedisease. Next would be patients with no currently detectable disease,negative for abnormal cytologies, a previous history and hematuria. Thenext group would be subjects having hematuria and without a previousdisease history. At lowest risk is a general population.

A marker sensitive to early changes would show a graded, parallelfraction of abnormality, while a late marker might only be abnormal inthe highest risk group. F-actin has been shown to be an early marker bythis approach, and over 90% of the subjects with biopsy-proven cancershowed F-actin-positive results. This approach represents a majoradvance in that it can provide extensive information about how markersrelate to each other, to the time frame and genetic changes involvedwith carcinogenesis, and the sensitivity and specificity for identifyingpremalignant or malignant disease in specific populations, includingsymptomatic and asymptomatic. Thus, carefully selected markers can beevaluated in long-term follow up studies knowing which markers areindependent and which are early or late markers.

EXAMPLE 2 Comparison of Results Between Immunochemistry and QuantitativeFluorescence Image Analysis

The DNA ploidy and p300/M344 results obtained on the same samplesprepared using immunochemical methods and QFIA methods were compared.The immunochemical samples were prepared using the Feulgen DNAdetermination and light absorption microscopy. The immunochemicalp300/M344 analysis was based upon alkaline phosphataseimmunohistochemistry with manual scoring rather than fluorescence, butwas interpreted against the same positive (≧5 cells positive forM344/10,000 cells) criterion used to interpret the QFIA samples. TheQFIA p300/M344 results were determined using both the automated programwith manual confirmation and an entirely manual scoring.

There was complete agreement in ploidy assessment in 20/29 cases (69%).Chi-squared analysis showed a high correlation (p=0.039) between the twosets of results. The agreement in p300/M344 analyses was, in contrast,poor. Of 13 samples, there was agreement on 8. Of the 5 disagreements,QFIA found 4 to be positive that had been scored as negative usingimmunochemical methods. One of the immunochemical samples was scored aspositive but negative using QFIA. Chi-squared analysis showed thecorrelation to be poor (p=0.28) between the two methods. These resultsindicate a significant difference in the potential outcomes of analysesusing the two methods.

EXAMPLE 3 Bladder Cancer Screening Using QFIA of Biomarkers

The screening was carried out in two phases, a pilot study in one of thecities to identify problems and test protocols, and the full screening,which amounted to 1686 exposed subjects and 388 controls. A total of2084 exposed subjects and 439 controls were notified, so that 81% and88% respectively of each group participated. Complete questionnaireinformation, blood, and urine samples were collected from all thesubjects, and shipped to the processing lab.

The experimental design of urine QFIA analysis optimized the usage ofurine sample and included as markers, DNA ploidy, G-actin, andp300/M344. The aliquot of urine sample for QFIA was not split, and allof the sample was fixed with 0.5% PF for 15 minutes followed by an equalvolume of 50% EtOH MOPSO fixative. Samples fixed in this manner werestored and shipped to the QFIA laboratory at 4° C. Two slides wereprepared from each specimen and were triple-labeled with M344 monoclonalantibody conjugated with Bodipy fluorochrome, DNase I conjugated withbiotin to measure G-actin for avidin-biotin immunofluorescence withTexas Red fluorochrome, and the DNA stain, Hoechst 33258. One slideserved as the negative control. DNA and G-actin were quantified usingthe Zeiss IBAS and the software of the present invention, whilep300/M344 was scored visually.

The slides were stored at -70° C. until analysis was begun using a ZeissIBAS system. The distribution of positive findings by exposure tobenzidine and smoking is listed in Table VI for a subset of subjects.The data set was combed to eliminate all samples that had incorrect orincomplete data. Previous smokers were analyzed with smokers, and nocorrection for quitting smoking was included. Exposure was calculatedboth on the basis of years of exposure and from an exposure index. Eachof the 12 job titles found to involve benzidine exposure was weightedaccording to the relative incidence of bladder cancer among workers withthat job title. Exposure index was calculated by summing the time workedat a job multiplied by job risk over the entire work history. However,exposure index calculated in that way showed no correlation withbiochemical markers or with any other markers such as hematuria. Somecorrelation was obtained with arbitrary weights of 1, 2 and 3 for low,medium and high exposures, but this may reflect no more than anassociation with years of exposure.

                  TABLE VI                                                        ______________________________________                                        Distribution of positive marker test                                          results by benzidine exposure and smoking history.                            Group                                                                         Smokers             Non-Smokers                                               G-Actin    DNA      M344    G-Actin                                                                             DNA    M344                                 ______________________________________                                        Exposed                                                                              61/375  35/384   13/384                                                                              22/130                                                                              14/130 5/130                                     16.3%   9.1%     3.4%  16.9% 10.8%  3.8%                               Controls                                                                             5/70    8/73     2/73  4/37  1/39   2/39                                       7.1%    11%     2.7%  10.8%  2.6%  5.1%                               ______________________________________                                    

The number of abnormal test results in the unexposed, non-smoking groupis small, and that is an apparent association with exposure. Most of thedifferences between exposed and nonexposed populations are highlystatistically significant, but rather than present a large number ofdifferent χ² comparisons, we elected to test all of these factorssimultaneously in the Cox Logistic Regression model as shown in TableVII and simultaneously test other factors such as prostatic hyperplasiaand hematuria. Smoking, as measured by pack years, and years of exposureto benzidine are the most significant variables in producing abnormalfindings. In the absence of carcinogenic exposure, smoking produces alarge effect on DNA ploidy. Other studies in our laboratories suggestthat the abnormal cells found in current smokers may not bepremalignant. It is likely that smoking produces an immediateclastogenic response that leads to failed cellular divisions withconsequent highly abnormal cells with increased DNA content. The vastmajority of such cells are terminal mutants that do not divide, and thenumber of such cells decreases to normal levels after smoking cessation.However, in persons with a previous history of cancer or other strongevidence for field disease, such cells are the abnormal offspring ofaltered basal cells and are, hence, truly dysplastic.

                  TABLE VII                                                       ______________________________________                                        Significance (p) of risk factors in                                           producing positive test results by                                            test using Cox Logistic Regression Model.                                                 p Value                                                                         G-Actin    DNA     M344                                         Feature       92/612     58/626  22/626                                       ______________________________________                                        Pack Years    .0001      .0001   .0001                                        Years of Exposure                                                                           .0001      .0001   .0001                                        Prostatic Hyperplasia                                                                       .0469      .0176   .0002                                        Exposure Index                                                                              .2154      .9746   .7379                                        Hematuria     .3215      .4558   .6964                                        ______________________________________                                    

Of interest was the strong association of prostatic hyperplasia as arisk factor, particularly with the p300/M344 test. In part this mayreflect increased risk for bladder cancer but outlet obstruction canproduce positive findings as well. Interestingly, hematuria was notassociated with positive marker analyses in this study, possibly becauseso many subjects were positive for hematuria.

The data were analyzed to determine optimal thresholds to use ininterpreting findings among this cohort of workers. The ROC plot for DNAis shown in FIG. 8. FIG. 9 shows the corresponding ROC plot for G-actin.As with the DNA ploidy marker, the exposed and nonexposed populationsdiffered in their distributions between the two populations. The unitsfor G-actin are reported as the ratio to a standard cell populationknown to express elevated levels of G-actin, expressed as a percentage.The threshold chosen in this study defined a positive such that the meanG-actin content of a minimum of 20 exfoliated cells from a single slidewas greater than 90 units. This definition of positive was used toderive the positive-negative findings listed in the tables.

A number of subjects had more than a single marker positive, and suchoccurrences were much more frequent than by chance (16-fold for allthree markers), thereby supporting the association between markers andexposure. The numbers of samples positive for two and three markersrespectively are shown in Table VIII by exposure and smoking.

                                      TABLE VIII                                  __________________________________________________________________________    Samples positive for two or three                                             markers by exposure and smoking history.                                      Group                                                                         Smokers              Non-Smokers                                              G-Actin  DNA M344                                                                              All G-Actin                                                                           DNA M344                                                                              All                                          +        +   +   Markers                                                                           +   +   +   Markers                                      DNA      M344                                                                              G-Actin                                                                           Positive                                                                          DNA M344                                                                              G-Actin                                                                           Positive                                     __________________________________________________________________________    Exposed                                                                            10/368                                                                            5/384                                                                             8/368                                                                             3/365                                                                             3/121                                                                             2/130                                                                             5/121                                                                             2/119                                             2.7%                                                                              1.3%                                                                              2.2%                                                                              0.8%                                                                              2.5%                                                                              1.5%                                                                              4.1%                                                                              1.7%                                         Controls                                                                           1/69                                                                              0/73                                                                              1/69                                                                              0/69                                                                              1/36                                                                              0/39                                                                              1/36                                                                              0/36                                              1.4%                                                                                0%                                                                              1.4%                                                                                0%                                                                              2.8%                                                                                0%                                                                              2.8%                                                                                0%                                         __________________________________________________________________________

Of particular interest were 22 subjects of the study who werep300/M344-positive. Each of these subjects was also positive for eitherG-actin or abnormal DNA. Thus, this particular marker seems to have avery strong association with other positive findings.

The Cox Logistic Regression model was applied to the multiply-positivesamples with results as shown in Table IX. The results were similar tothose obtained with single markers. Pack years of smoking and years ofexposure were the most significant variables, while prostatichyperplasia was significant as well. Hematuria made no contribution tothe model in this particular study, and the contribution of the exposureindex was small.

                  TABLE IX                                                        ______________________________________                                        Logistic regression analysis of multiply                                      positive samples by other risk factors.                                               p Value                                                                         G-Actin   DNA      M344    All                                                +         +        +       Markers                                            DNA       M344     G-Actin Positive                                 Feature   15/579    7/619    15/579  5/594                                    ______________________________________                                        Pack Years                                                                              .0001     .0001    .0001   .0001                                    Years of  .0001     .0001    .0001   .0001                                    Exposure                                                                      Prostatic .0008     .0001    .0001   .0001                                    Hematuria                                                                     Exposure  .4395     .2907    .9703   .2831                                    Index                                                                         Hematuria .8624     .6397    .9093   .8119                                    ______________________________________                                    

The association of Papanicolaou cytology with the other markers and withother risk factors was also investigated using the Logistic Regressionmodel with results as shown in Table X. There were 48 positive findings,with positive, suspicious, and atypical all considered as positivefindings in this analysis.

Preliminary data analyses have established that the marker set employedis capable not only of detecting tumors but of predicting tumors becausethe tumors that were found were from subjects in whom no tumors wereevident at a previous screening.

                  TABLE X                                                         ______________________________________                                        Logistic regression analysis of Papanicolaou cytology                         comparing other markers and other risk factors.                                                p Value                                                                       Pap Cytology                                                 Feature          48/687                                                       ______________________________________                                        G-Actin Mean     .0001                                                        M344 Pos/10K     .0001                                                        DNA >5 C %       .1346                                                        Pack Years       .0002                                                        Years of Exposure                                                                              .0234                                                        Prostatic Hyperplasia                                                                          .2661                                                        Exposure Index   .9714                                                        Hematuria        .0974                                                        ______________________________________                                    

Methods

a. Quantitative Fluorescence Image Analysis. Three markers weresimultaneously analyzed. Each sample was centrifuged and the pelletresulting was taken up in buffered fixative and frozen (-70° C.) untilanalyzed. This procedure preserves marker quantitation and cellmorphology. To further minimize the number of inadequate samples, thecells in the sample were counted on a Coulter cell counter usingalgorithm that takes into account crystals, if present, small cells,such as lymphocytes, and large urothelial cells. This count is used todetermine whether the cells will be aliquoted into one container or two.The samples are thawed, diluted with buffer, collected onto a filter andimprinted onto a special slide adapted for use on a Code-On automatedstainer/labeler. These methods are described in more detail in thesections "Fixative/Preservative Solution" and "Slide Preparation".

The programmable robotics staining/labeling device precisely andreproducibly steps paired slides separated by a thin space through thereagents needed to label the slides with immunologic and dye reagents.Each slide was triple-labeled for, (1) DNA using 8.7 μM Hoechst 33258,(2) p300 using the M344 mouse monoclonal antibody direct-conjugated withBodipy fluorochrome, and (3) DNase I (Molecular Probes) directlyconjugated to Texas Red to detect G-actin. A corresponding negativecontrol slide omitting primary antibodies was also prepared. Allreagents had been optimized to achieve saturation. With each batch ofslides, cell lines known to be high and low expressors for eachquantitative marker were also included.

Markers were quantified on individual cells on a cell-by-cell basis for50-100 cells per slide using a Zeiss IBAS image analysis system equippedfor quantitative fluorescence. A schematic diagram of the IBAS system isshown in FIGS. 12A-C (neural net computer was not used in this example).In tumor biopsies, regions of tumor cells were specifically analyzed,but cells were randomly selected in nontumor control specimens. In bothcases infiltrating lymphocytes or blood cells were specificallyexcluded. The images of labeled cells were captured and the intensity ofeach pixel (dot comprising the image) was converted to a digital greylevel between 0 and 255. Immunofluorescence was measured as theintegrated grey level (IGL) or averaged grey level (AGL). AGL is theaverage grey level of the pixels comprising an image and is proportionalto the average concentration within a cell. IGL is the value obtainedmultiplied by the area of the cell image and is proportional to thetotal content of marker in each cell.

The immunofluorescence assays were calibrated against cell lines knownto express high and low amounts of the proteins. A large number ofslides were prepared from a single batch of each cell line. A positive(with primary antibody) and negative (without primary antibody) slidefor each marker was included with each batch of patient samples, and thecells were labeled and analyzed as described. The IGL or AGL wascorrected for extraneous background fluorescence by subtracting the meanIGL or AGL determined from approximately 100 cells on the negativecontrol slide.

The p300 marker was scored visually by two independent observers. Asample was considered positive if more than two cells positive for p300per 10,000 cells were found on the M344-labeled slide. The count ofcells on the slide was obtained by the IBAS. The positive cells weremanually confirmed. G-actin was approximately normally distributed. ForG-actin, a sample was considered positive if the mean IGL was greaterthan 90% of the mean IGL of a positive-expressor cell line. Assays forp185 and EGFR have also been developed. For p185, the mean IGL of cellson the negative control slide is calculated and subtracted from the IGLof each cell on the sample side. The p185 shows a lognormaldistribution. If the adjusted mean IGL significantly (t-test) exceedsthe adjusted IGL of a low expressing cell line (3T3 SW480), the sampleis labeled as positive. For EGFR, a histogram of normal cells fromcontrol patients is constructed, and the presence of cells above theupper limit of normal is used as an indicator of positive.

b. Papanicolaou cytology was performed using standard, routine urinarytract cytological methods.

c. Urinalysis and Hematuria Testing was performed using standardtechniques, including a dipstick for heme. Urine sediment analysis wasperformed within 2 hours of collection of the sample.

Statistical Analysis

Preliminary statistical analysis of the decoded data from identifiedbenzidine exposure, smoking, and prostatic hyperplasia as significantrisk factors for the development of abnormal marker results. Not alldysplasias will necessarily progress to clinically detectablemalignancy. Thus the number of marker-positive findings at time t willalways be greater than the number of malignant lesions found in the samepopulation and higher than the number of malignant lesions found uponsubsequent follow up. In addition, only a portion of eventual disease,whether malignant or premalignant, results from known exposures, so thatpositive findings are expected in persons without known carcinogenicexposure.

Fixative/preservative solution

This fixative/preservative solution preserves the cells with retentionof characteristic morphology and quantity and concentration anddistribution of biomarkers in the cells while simultaneously inhibitingthe formation of crystals in the urine. This process of inhibition ofcrystal formation is important because crystals can (1) prevent orinterfere with adherence of cells to a slide, (2) lengthen the timenecessary to prepare the slide for analysis, and (3) physically obscurethe viewing of cells on the slide. The benefits to the reduction ofcrystal formation in the sample are to (1) decrease preparation time,(2) decrease the number of unsatisfactory slides which are produced, and(3) increase the number of cells per microscope field on the slide.

A study was conducted in which 50 slides were prepared from urinesamples fixated with the crystallization inhibiting fixative of thepresent invention, and separately 50 slides were prepared from urinesamples fixated with a non-crystallization inhibiting control fixative(MOPSO with buffered ethanol, see, for example the reference by McGowan,et al., cited previously). The urine samples were taken from the samplepopulation . On the slides using the crystallization inhibitingfixative, there was an average of 165 cells per mm². On the slides usingthe control fixative without crystallization-inhibiting agents, therewas an average of 33 cells per mm². Thus, inhibition of crystallizationimproved adherence of cells to the slide by a factor of 5x.

The primary crystals inhibited by the method are common crystalscomprising calcium and magnesium cations. Formation of certain rarecrystalline forms may not be inhibited.

The term "inhibition of crystal formation" as used herein is defined asmeaning the inhibition of the formation of crystals containing calciumor magnesium, or the solubilization such crystals which are alreadypresent, in urine samples which would otherwise form crystals under arange of temperatures including room temperature and refrigerationtemperatures (e.g., about 4° C.) and during a range of time periodsincluding immediately after collection of the sample, after 24 hours andafter 48 hours or later if fixation was performed using a commonly usednon-crystallization inhibiting composition such as buffered alcohol.

Inhibition of crystal formation allows the urine to be stored andshipped with its cells preserved. The fixative is designed to be mixedin equal volume with the urine. In a preferred version, the fixativeconsists of four components with an optional fifth.

A first component is a preservative which kills most bacteria and othermicroorganisms and inhibits endogenous enzymatic degradation. A specificpreservative is ethanol (50% v/v). A second component is a buffer toadjust the pH of the solution to help retain morphology, the bufferhaving a pK preferably in the range of 6-7 but alternatively in theranges of 5-6 or 7-8. A preferred buffer is2-N-morpholino-2-hydroxypropanesulfonic acid (MOPSO), 0.05M, which isalso effective within a range of from about 0.01M to 0.2M.

Other buffers in the preferred pk range are N-2-Acetamido!-2-aminoethanesulfonic acid) (ACES), (N-2-Acetamido!-2-iminodiacetic acid) (ADA), (bis 2-Hydroxyethyl!imino-trishydroxymethane!methane (BIS-TRIS), (2- N-Morpholino!ethanesulfonic acid(MES), and (Piperazine-N,N'-bis 2-ethanesulfonic acid! (PIPES). Buffersin the alternate pk range of 7-8 are (N,N-bis2-Hydroxyethyl!-2-aminoethanesulfonic acid) (BES), (3-N,N-bis(2-Hydroxyethyl)amino!-2-hydroxypropanesulfonic acid) (DIPSO),(N- 2-Hydroxyethyl!piperazine-N'- 3-propanesulfonic acid! (EPPS), (N-2-Hydroxyethyl!piperazine-N'- 2-ethansulfonic acid! (HEPES), (N-2-Hydroxyethyl!piperazine-N'- 2-hydroxypropanesulfonic acid!) (HEPPSO),(3- N-Morpholino!propanesulfonic acid) (MOPS), (Piperazine-N,N'-bis2-hydroxypropanesulfonic acid!) (POPSO), (3N-tris(Hydroxymethyl)methylamino!-2-hydroxypropanesulfonic acid)(TAPSO), and (N-tris Hydroxymethyl!methyl-2-aminoethanesulfonic acid)(TES). These buffers are commercially available from a source such asSigma Chemical Co.

A third component is a substance for inhibiting formation, and forsolubilization, of crystals. Where the preservative is ethanol, thesubstance should be soluble in ethanol. A specific example is thedipotassium salt of ethylenediaminetetraacetic acid (EDTA). Other EDTAsalts of cesium, rubidium, and various organic cations may also beeffective, as are other salts, derivatives and analogs.

A fourth component is a substance to maintain the ionic strength withinlimits that inhibit cell distortion. A specific example is KCl, 0.10M,and it must be both soluble in the preservative (e.g., ethanol) and notcause precipitation of the solubilizing agent (e.g., potassium EDTA).Alternatively, the substance to maintain the ionic strength may be anadditional amount of the buffer previously added or another compatiblebuffer.

A fifth component, which is optional, is a biocide to prevent the growthof certain resistant bacteria and other microorganisms. A specificexample is NaN₃, sodium azide, 0.1% (w/v). This biocide component isoptional depending on the time lapse between collection and shipment andanalysis. The fixative is prepared by mixing the four aqueous componentsand adjusting the pH to 6.5, then adding an equal volume of pureethanol.

Additional fixation to ensure preservation of protein markers can beachieved by first mixing the urine sample with a formaldehyde solutionto a final concentration of 0.5% (w/v) and allowing theurine/formaldehyde mixture to stand for a time period, e.g., 15 minutes,prior to addition of the above fixative.

The efficacy of the crystallization inhibiting formulation of thepresent invention is shown in the photomicrographs of FIGS. 10A and 10Band 11A and 11B. The fixative of the present invention was compared to anon-crystallization-inhibiting fixative. A first urine sample wascollected. A portion of urine sample was fixated with fixative of thepresent invention having the crystallization inhibiting fixativecomprised of 50% ethyl alcohol and MOPSO buffer. The first urine samplecontained an abundance of oxalate. A phase contrast photomicrograph ofthe urine sample fixated with the non-crystal inhibiting fixative isshown in FIG. 10A. A phase contrast photomicrograph of the portion ofthe urine sample fixated with the crystal inhibiting fixative of thepresent invention is shown in FIG. 10B.

Similarly, a second urine sample, containing an abundance of amorphousphosphate crystals was collected. A portion of the second urine samplewas fixated with the non-crystal inhibiting fixative and another portionof the second urine sample was fixated with the crystallizationinhibiting fixative of the present invention. A phase contrastphotomicrograph of the portion of the second urine fixated with thenon-crystal-inhibiting fixative is shown in FIG. 11A. A phase-contrastphotomicrograph of the portion of the second urine sample fixated withthe crystallization inhibiting fixative is shown in FIG. 11B.

FIG. 10A shows a urine sample containing an abundance of oxalatecrystals, interference of the cells on the slide by the crystalspresent, and only about 1 to 2 cells per microscope field.Alternatively, FIG. 10B shows a urine sample containing only rarecrystals and containing significantly more cells than the numberindicated on the slide micrograph of FIG. 10A.

FIG. 11A shows a urine sample containing an abundance of amorphousphosphate crystals, obfuscation of the cells on the slide by thecrystals present, and only about 1-2 cells per microscope field.Alternatively, FIG. 11B shows a urine sample containing only rarecrystals and containing substantially more cells than the numberindicated on the slide micrograph of FIG. 11A, and which aresubstantially more visible.

Sample Slide Preparation

In order to prepare a sample for slide preparation, it must be thawed toroom temperature if previously frozen. Label a washed 50 ml centrifugetube for each sample. Label an Accu-vial for each sample and fill with20 ml of Isoton using a repipettor. When the sample is thawed, shake itvigorously and pour it into a respective 50 ml centrifuge tube. With aclean disposable pipette, vigorously aspirate the cell suspension todisperse the clumps. Put approximately 2 ml of filtered DDW solutioninto the empty freezer vial. Aspirate the solution three or four timesto break up the clumps. Transfer the solution to a respective 50 mlcentrifuge tube containing the thawed cells. Fill to the 45 ml mark with50% of EtOH+5 mM EDTA solution. Shake the 50 ml tubes and let stand atroom temperature for 20 minutes.

In preparation of analyzing the sample with a Coulter counter, aliquot0.5 ml from the sample in the 50 ml tubes into filled Accuvials. Preparethe Coulter Counter as per protocol. Perform a background count. If thereading is less than 5, proceed. If the reading is more than 5, wait aperiod of time for any bubbles in the blank to disperse and count again.If the reading is still more than 5, clean the aperture, rinse the probeand refill the blank container with Isoton after rinsing the containerthoroughly with DI water. Gently invert a sample three or four times.Read the sample on the Coulter Counter at the following lowerthresholds: 5.138 microns, 10.03 microns, and 15.00 microns. If the5.138 micron count is equal to or greater than 1000, centrifuge thesample again in the 50 ml centrifuge tube. Pour off the supernatant.Raise to 45 mls with 50% of the EtOH+5 mM EDTA solution as before. Letthe sample sit 20 minutes at room temperature. Recount on the Coulter asbefore.

To apply a portion of the cell sample to a slide, mount an 8 micronNucleopore (or polycarbonate) filter on the Millipore Filter Apparatususing the small bore base with a 15 ml acid cleaned funnel, takingreasonable care to center the filter on the fritted base, removing allthe wrinkles. Mount the funnel such that the filter is not moved orwrinkled. Shake the sample vigorously. Pour the calculated volume intothe funnel. Note: If the count at 5.13 is >400, double the volume. Usingthe gentle vacuum, draw down the sample in the funnel to just above thefilter. Pour 10 mls of the DDW+5 mM EDTA solution into the funnel. Drawthe liquid in the funnel down to the filter with the vacuum, being surenot to air dry the filter (go slowly near the end). Pour 5-10 mlsSaccomanno Fixative into the funnel. Vacuum 1-2 ml through then let itstand 2 minutes. Label two polylysine coated Probe-On slides with thesample Lab Number and the Study Number in pencil. Label one slide aspositive (+) and the other slide as negative (-). Place the (+) slide onthe imprint guide. Put a couple of drops of DDW on the (-) slide. Whenthe time is up, apply the vacuum to the funnel removing the fluid suchthat the bottom of the meniscus falls just below the center of thefilter, taking care not to air dry the filter (filter slowly near theend). Quickly remove the funnel by carefully tilting it back and awaybeing sure the filter is left on the stand.

Grasp the filter with a clean pair of filter forceps. Invert the filterand center it on the (+) slide aiming for the target of the imprintguide. Place a folded dampened Kimwipe over the filter and the slide,then press gently with the side of the hand for approximately 7 seconds.Peel back the filter with filter forceps. Lay the filter cell side downon the drop of water on the (-) slide. Quickly spray the (+) slide withCarbofix-E. Press the filter on the (-) slide with a folded Kimwipe for7 seconds. Peel back the filter and spray the slide with Carbofix.

If enough of the sample is left to at least double the amount used tomake the slide, check the slide for the correct number of cells under amicroscope and remake the slide with an adjusted volume if too sparse ortoo dense. Record the final volume used to prepare the imprint on theprep worksheet. Freeze the imprinted slides at -20° C. in numericalorder.

Freezing Leftovers:

The volume must be at least the amount used to prepare one slide fromthe prep worksheet in order to be useful in the future. Centrifuge thesample at 600 g for 10 minutes. Pour off the supernatant. Transfer thepellet to a 5 cc cryovial labeled with the lab number and the studynumber. Place in the refreeze box recording box. Freeze the samples at-70° C.

Reagents:

5 mM EDTA DDW solution: 7.44 g EDTA disodium salt in 4000.00 ml ofdeionized distilled water. Stir until dissolved. Adjust the pH to 5.5.Filter through a 0.22 μm magna nylon filter. The solution is stable for1 week at 4° C. Alternately, the filtered solution may be filtered andstored at -20° C. When ready to use, thaw and adjust pH prior to use.

50% ETOH in 5 mM EDTA solution: 7.44 g EDTA disodium salt and 1894.74 mldistilled deionized water. Filter 2105.26 ml 95% EtOH through a 0.22 μmnylon filter. Stir the EDTA solution until dissolved. Adjust the pH with1N HCl to 5.5. Add to the filtered EtOH solution by adding the EDTAsolution to the filter apparatus and filtering.

Modified Saccomanno Fixative:

Combine 20 ml of Polyethylene glycol (PEG) 1540 (e.g., Union Carbide),516 ml of 95% ETOH, and 464 ml of Buffered Filtered Saline (BFS). MeltPEG at 60° C. Prepare 50% ETOH solution. Slowly add 20 ml of melted PEGsolution to the ethanol solution while stirring. Stir for one hour.Store at room temperature.

To Prepare the Poly-L-lysine Coated Slides:

Load the slides in the slide rack. Wash the slides by soaking one hourin hot 2% Alconox solution, rinsing one hour in running hot water, andrinsing one hour in running distilled water. Dip the washed slides twotimes in 0.25 Ammonium Acetate. Dip two times in distilled water. Soakin poly-L-lysine for 10 minutes at room temperature. Air dry indust-free environment or in 50° C. oven. Store in dust-free boxesindefinitely. The Poly-L-lysine working solution may be stored at 4° C.and reused for 1000 slide capacity.

Materials for coated slides:

Combine 0.1 g of poly-L-Lysine (e.g., Sigma #1524, MW>300 KD) and 2000.0ml 10 mM Tris-HCI buffer (pH 8.0) to make the Poly-L-lysine workingsolution.

Combine 2.42 TRIZMA Base (e.g., Sigma Chemical Co. #T-1503) with 2000.0ml distilled H₂ O) to make 100 mM TRIS Buffer. Titrate to pH 8.0 with1.0N HCI.

Combine 19.27 g Anhydrous Ammonium Acetate and 1000 ml of distilled H₂O) to make 0.25M ammonium Acetate.

OFIA Software and Methodological Description

The image analysis system consists of an epi-fluorescence microscopeequipped with motorized stage, autofocus mechanism, filter wheelcontaining various degrees of interference neutral density filters,custom built 4FL excitation/emission filter changer, motorized shuttersand objective magnification changer (FIGS. 12A-C). All microscopecomponents are controllable by software from the IBAS console as well asmanual controls at the microscope. The software applications developedfor this instrument use some common modules i.e. initialization of allmotors, scan parameters, case file creation, restoration of incompleteruns, location of first field, automatic threshold selection, DNA scenesegmentation, artifact rejection, DNA quantification, image clipping,image review and rejection, and image and data storage. The featuresselected for measurement, pre-processing of images, scene segmentation,threshold selection, and which constitute features of a cell of adesired type, are determined by the specific marker of interest.

An example of the program flow for a double-label assay, such as for DNAand G-actin is shown in FIGS. 13-14B. Briefly described, all motors areinitialized when the instrument is powered up. In this process, home islocated and defined for all motors and methods of communication todevices are established where appropriate. The meander pattern desired,optimal neutral density filters and exciters for each marker, and numberof desired images of each marker are input by the user. The optimalfocus is established in a brief training session for DNA. The casesloaded on the multiple position slide stage are then input into a scrollscreen customized for quick entry. This information includes thefilenames to be used for data and image storage, laboratory accessionnumbers to be stored with individual cell measurements, and patientsnames for each loaded stage position.

Upon completion of data entry, the software takes control and operatesunattended until all cases have been completed (FIGS. 14A-B). Duringthis time, selected features are measured and stored in temporarydatabases while images are clipped and stored to optical disk. The scanon each case continues with movement of the X and Y motors, focus, imagecapture, artifact rejection, image enhancement of each field for bothmarkers simultaneously until enough cells have been measured (i.e.,definitive positive or the entire slide).

The operator is then presented with a gallery of selected cells in orderto exclude additional artifacts for each case. Alternatively, thegallery is sent to a neural net computer trained to exclude additionalartifacts. The manual operation is controlled by the digitizer with apoint and click approach to each image. The final histogram is presentedwith options to adjust the DNA 2C peak based on internal control cellsif desired. Rejection of artifacts for both markers is included withsubsequent production of the final report on a laserjet printer.Permanent databases are created and stored in the data path directory.The program then loops back to the case load module allowing additionalslides to be analyzed.

In the event that a power failure occurred or the software is terminatedwhile analyzing samples, a module is included to restore incomplete datain which temporary files created during the scans are restored, imagesreviewed, final reports generated, and permanent databases created.

The dual-marker software module for rare event scanning (M344-DNA) (FIG.15) is similar to that of G-actin/DNA except that two passes are made oneach slide during M344/DNA analysis with the first pass performed at lowmagnification (FIGS. 16A-16B) and the second pass at high magnification(FIG. 17).

This software is capable of locating events that occur in 2 per 10,000cells. M344 is scored as a presence or absence marker with the number ofevents of expressing cells among the total number of epithelial cellscalculated.

The triple label software (FIG. 18) for touch preps has been designed tobe interactive with the user in selection of cells and fields. It hasadditional features which allow the user to separate cells in tissuefragments using the digitizer. The three markers from each selected cellcan be simultaneously measured with data and image storage if desired(FIGS. 19A-B). The software allows for image retrieval of stored imagesor direct image capture of live images from the microscope. Dataanalysis is also interactive with user selected parameters, scales, andthree-dimensional histograms of selected parameters.

Referring in more detail to FIGS. 16A-B, the Rare Event scanning beginsat low magnification. A portion of the prepared slide, such as a singlefield at low power is located and irradiated with a wavelength of lighteffective in irradiating the dye labeled to DNA. The resulting fieldimage of the microscope field is digitized and analyzed for objectimages which are images of objects within the criteria established forcells. If the field image is the first which appears to contain cells, agrey value threshold for DNA is calculated. Another review step of thefield image is conducted by the microscope wherein object images whichexceed a predetermined size threshold or which are smaller than aminimum size threshold are rejected as artifacts.

The minimum size threshold for nuclei of all cells is set at ≧35μ². Themaximum size threshold for all cells is <600μ². For a cell to becategorized as a morphologically transitional cell the minimum nucleussize is ≧45μ². For a cell to be categorized as a morphologicallyabnormal cell the nucleus size is ≧60μ². The size limits of thecytoplasmic portion of all cells are ≧200μ² and ≦4000μ².

Object images which survive this review step undergo further analysis.In particular, object images are reviewed more slowly to examine thenuclei. At this stage objects which lack nuclei, such as cell fragments,or have more than one nucleus are rejected. At this stage, cellsabnormal for DNA and tissue fragments with DNA are detected and theircoordinate locations are recorded. Similarly, the field is irradiatedwith an excitation wavelength for the M344-conjugated fluorochrome. Theresulting field image is corrected for sources of extraneousfluorescence such as background fluorescence and autofluorescence, andfor camera shading. The field image is then segmented into discreteobject images which are reviewed for a positive appearance for M344(i.e., fluorescence intensity exceeds a predetermined threshold) andwhich satisfy desired morphological requirements for size and shape. Ifpositive object images are identified, their coordinate locations arerecorded. Images of these selected cells may be stored. Positive objectimages are searched for until the entire slide has been examined oruntil a predetermined minimum number of recorded M344 positive imageshas been exceeded. An example of such a predetermined threshold is 20positive images per 5,000 cells on the prepared slide.

Once the field image has been searched under the first magnification(low power e.g. 12.5× power for DNA, 25× for most other markers), eachobject image identified as abnormal image is viewed again under asecond, higher, magnification (e.g. 25× power for DNA) (FIG. 17) duringwhich the high power object image is reviewed again for comparison withpredetermined selection requirements and DNA is quantified. For example,objects showing cells with nuclei touching or other cell fragments areeliminated from consideration. Object images which pass this selectionprocess are reviewed again more slowly and are compared to selectionparameters related to cell shape. Additionally, DNA is quantified atthis point. If the object images survive this review, and are stillconsidered to be morphologically abnormal, or have DNA in excess of apredetermined amount (e.g., ≧5C), object images are stored.

The slide is searched for positive object images until the entire slidehas been examined or until a predetermined number of cells with abnormalDNA has been recorded (e.g. until the gallery is full of stored images).Under high magnification, a random scan for cells with abnormal DNAcontinues until at least 100 cells are measured. In the case of G-actin,the scan continues until 100 cells have been measured for G-actin oruntil the DNA scan is complete.

The stored object images of abnormal cells, cells with abnormal amountsof DNA or cells positive for M344 (or positive for a similar marker, orhaving particular quantities thereof) may be reviewed again by anothermethod for confirmation. For example, the object images may be confirmedby a trained operator, or the stored object images may be delivered toan automated confirmation system such as a neural net computer trainedwith a library of normal, abnormal, positive, negative and falsepositive cell images.

Autofluorescence

For unknown reasons cells from urine often fluoresce even in the absenceof added fluorophore. This autofluorescence can introduce error into theresult because the fluorescence emitted from a given portion of alabeled cell will be the sum of autofluorescence plus that light emittedfrom the bound probe. One method of accounting for autofluorescence isto determine the average autofluorescence of some number of cells on anegative control slide to which no probe has been added. This averageautofluorescence (as IGL) is then subtracted from the IGL of eachexamined labeled cell on the slide.

Unfortunately, these methods of accounting for autofluorescence errorare not adequate. Autofluorescence occasionally varies widely from cellto cell in a population of cells. For example, a minority of cells maybe very bright by autofluorescence, thus adding an element of error tothe average. In addition, autofluorescence may be unevenly distributedwithin a single cell. Some methods raise the threshold intensity ofemitted light which is measured. However, this method decreases thesensitivity of the of the measurement resulting in the possibility thatsome "low expressing" cells may be "lost". An accurate method ofaccounting for autofluorescence would increase the accuracy andprecision of fluorescence analysis.

Correction for Autofluorescence

In the present invention, the preferred method for accounting for errordue to autofluorescence is to measure the autofluorescent light emittedfrom each cell upon excitation by a predetermined excitation wavelength.The grey level image of the cell is then corrected on a pixel-by-pixelbasis.

Each fluorochrome used to label a particular marker has a known spectralresponse and has a characteristic excitation wavelength which causespeak fluorescence emission (see Table IV). When the cell labeled withthe fluorochrome is irradiated with the excitation wavelength, the totalfluorescent light emitted from the cell is comprised both of lightemitted from the fluorochrome, and of light emitted from othercomponents of the cell. The latter is the autofluorescent component ofthe total emitted fluorescence. To obtain an accurate measurement of thelight emitted from the label, the fluorescent portion must be subtractedfrom the total emitted light.

This could be done by calculating an average autofluorescence over theentire image, then subtracting the same average amount ofautofluorescence from each pixel of the image. However, theautofluorescent component can vary from pixel to pixel. Therefore, amethod which subtracts only an average autofluorescent value can stillresult in significant error in any given pixel. To determine the amountof autofluorescence for a given pixel a second excitation wavelength ischosen from the tail of the fluorochrome's excitation spectrum. Thiswavelength is significantly different from the peak excitationwavelength of the fluorochrome (FIG. 20).

When the slide is irradiated with this second autofluorescencewavelength, most of the fluorescent light which is emitted isautofluorescence, with a small portion being emitted from thefluorochrome. In effect, the autofluorescence wavelength mildly excitesthe fluorochrome but does not cause a high level of excitation such asis caused by the peak excitation wavelength for the fluorochrome. Thefluorescence emission is then digitized for each pixel in the image.This fluorescence is mostly autofluorescence.

Each calculated autofluorescence grey level is subtracted from the greylevel obtained for that pixel when the slide is irradiated with theexcitation wavelength of the fluorochrome. The resulting grey levelvalue is the pixel grey level corrected for autofluorescence andprimarily represents the quantity of the fluorochrome in that portion ofthe cell. Most of the autofluorescence is effectively removed from theimage. This correction is made for each pixel of each cell measured.

EXAMPLE 4 Risk Assessment with Biomarkers

The process of cancer development is slow but progressive in thatbiochemical changes in cells are frequently apparent well before aclinically detectable tumor is present. The transition from "normal" to"premalignant" to "malignant" is continuous in that there is no singleknown marker that is always positive in cancers and never positive inpremalignant lesions. Therefore one can only discuss "risk," which canhave several meanings. The medical endpoint is a detectable tumor, butit is very clear that tumors exist before they can be detected by theusual diagnostic tests. For a variety of reasons, these tests do notalways yield reliable results so that a false negative diagnosissometimes occurs. Therefore, laboratory testing provides two kinds ofinformation. One is "confidence," which is the probability that a givenprofile of markers establishes the presence of disease. The second is"risk assessment," which generally means an assessment of the patient'sfuture prospects, but which can include the concept of risk for thedisease. Thus, one can discuss the risk that a patient faces in havinghis or her disease progress to a more severe grade of disease, the riskthat a cancer will recur, or the risk that a cancer will develop. Bothsurety and risk assessment can be stated in quantitative probabilisticterms (e.g. 22.4% of patients with such a marker profile will developdisease with 5 years) or as discontinuous assessments of risk orprobability (e.g. "it is very certain that the patient has cancer" or"at moderate risk for recurrence"). The ideas of surety and riskassessment are not independent, and the same statement can contain bothsurety and risk assessment information. The statement that "74% ofpatients with these findings have a clinically detectable cancer"contains both surety information (i.e. we are 74% confident of thediagnosis) and a risk assessment (i.e. this person has a 74% risk forhaving cancer.)

An example of a risk categorization approach that has been developed foruse in the interpretation of QFIA cytology is shown in Table XI. Thisschema combines both the information inherent in the visual cytology andthe detection of aberrant cells with >5C DNA and combines both riskassessment and surety information. In fact, the DNA content data allowthose patients with "atypical" cells to be further classified into thosewhose atypical cells are cancer related (Group 4) and those who arelikely not (Group 2), leaving only a small intermediate group (Group 3).These categories correspond to: 1=no aberrant findings; 2=some abnormalfindings that are rarely associated with cancer but which more usuallyare associated with benign processes and if associated with cancer areassociated with very low grade, nonaggressive disease; 3=some abnormalfindings that are occasionally associated with cancer but usuallyreflect age, smoking or other processes and if associated with cancerindicate either a very low grade, nonaggressive disease or very earlystages of more aggressive disease; 4 and 5 indicate abnormal findingsassociated with low and high grade disease, respectively. The exactnumber of abnormal cells detected within a sample also providesconfidence information. A finding of 20 cells with >5C DNA and atypicalmorphology almost certainly establishes that a patient also has aclinically detectable cancer. The probability that a patient with only 3such cells has a clinically detectable cancer is obviously lower. Such asmall number of cells could have arisen in a premalignant lesion, asmall lesion not visible by cytoscopy, or could be the result of a fewfailed divisions in cells that leave no progeny. Furthermore, confidenceinformation can also be derived from the concordance among markers within single cells. The probability that the three abnormal cells citedabove arose from a cancer is higher if the cell also shows othercancer-related abnormalities that are independent of ploidy changes,examples being the tumor-related antigen, p300, and F-actin or G-actin.

                  TABLE XI                                                        ______________________________________                                        Definition of risk categories                                                 using visual cytology and rate of                                             appearance of cells with >5 C DNA                                             as determined with Hoechst 33258 dye.                                         Risk       Visual           Cells with                                        Category   Cytology         >5 C DNA                                          ______________________________________                                        5          Suspicious       ≧2/500                                                or positive      ≧2/500                                     4          Atypical         ≧2/500                                     3          Atypical         1-2/500                                           2          Atypical         0/500                                             1          Negative or      0/500                                                        viral changes                                                      ______________________________________                                    

The information in Table XI can be converted to a quantitative,probabilistic statement by (1) determining the proportion of patients ineach category that have clinically detectable tumors at the time thetest was performed and (2) following such patients for a time (e.g. 1year) to determine what proportion of those in whom a tumor could not befound developed a tumor within the time period chosen or what proportionof patients in each category developed recurrences after the chosen timeperiod. Table XI contains data from two markers (DNA and visualcytology). It could be readily expanded to include additional markers,such as those described in Example 1, without revising the categories.For example, addition of a tumor-related antigen measurement (e.g. p300as determined by M344 antibody) could allow many patients in Group 3that are associated with cancer to be categorized either into Group 2 orGroup 4.

An example of a probabilistic statement is provided in Table XII. Inthis study, QFIA cytology was performed on patients with known diseaseand compared to conventional Papanicolaou urinary cytology. Patientswere classified by the grade of tumor. A positive finding for QFIAcytology was a Risk Category 4 or 5 as listed in Table XI. These datashow that for the particular population of cancer patients selected,QFIA cytology performed better than did conventional cytology as well asproviding a quantitative estimate of the surety of a given finding asrelated to the grade of tumor that the patient had.

                  TABLE XII                                                       ______________________________________                                        Comparison of sensitivities (percent of patients with                         disease having abnormal findings) for QFIA cytology and                       Papanicolaou cytology in bladder cancer detection.                                      QFIA        Papanicolaou                                            Tumor Grade N     Sensitivity N   Sensitivity                                 ______________________________________                                        1-2         74    81%         86  52%                                         3-4 or CIS  52    100%        54  96%                                         ______________________________________                                    

An additional example is provided in Table XIII, which examines thequestion of what confidence can be placed in a positive finding. Thisshows that Groups 1-3 are predominately associated with benign findingsbut that the incidence of "false positive" findings increases with oneknown risk factor for bladder cancer, namely age. Smoking has a similareffect upon QFIA cytology as does bladder outlet obstruction and urinarytract stones, all of which increase the risk for cancer and producecytologic changes characteristic of aberrant cells. Thus, QFIA cytologyalso is providing a risk assessment in that patients with such aberrantfindings are shown to be at higher risk for developing bladder cancerthan are patients without such findings.

                  TABLE XIII                                                      ______________________________________                                        Aggregate data from several studies of asymptomatic                           controls showing percentage distribution by risk                              category as a function of age.                                                       Risk Category                                                          Age      N        5       4     3     2    1                                  ______________________________________                                        <50       57      0       4     4     33   60                                 ≧50                                                                             162      4       4     4     29   59                                 Total    219      3       4     4     30   59                                 ______________________________________                                    

A further point in the evaluation of marker profile measurements is thatthe marker itself may require validation, that is, it may be necessaryto determine the relationship between the marker and disease if thatmarker does not have a history of use in medicine. This problem has beenaddressed by the development of a means of rapidly assessing whether agiven new marker or combination of markers is useful for riskassessment. With this approach, a group of patients are stratified intogroups representing distinct different experiences. This approach isillustrated in Table XIV, which shows how the marker F-actin wasevaluated and determined to be a useful marker for assessing bladdercancer risk using a group of patients that had been stratified on thebasis of perceptions of risk that were based upon expert experience.Patients in Group One are obviously at the highest risk for havingcancer, given a positive cytology. In fact, all these patients hadbiopsy-proven disease. Group Two is symptomatic, having hematuria, andhas an intermediate QFIA cytology. This combination of factorsestablishes that the Group Two patients are at higher risk than GroupsThree or Four, and this is reflected by the high proportion (75%) ofsuch patients exhibiting abnormal F-actin. Patients having hematuria andnegative QFIA cytology, are next stratified by whether or not they had aprevious history of bladder cancer. Those with a previous history ofbladder cancer (Group Three) are at higher risk than those without(Group Four). This approach can be made quantitative by following suchgroups for some time period and determining the proportion that developcancer within given time periods. This approach illustrates theimportance of examining groups in addition to those with disease andthose who are perfectly normal because it is the patients who aresymptomatic who must usually be classified by any marker profiletesting, not completely asymptomatic patients. The only exception is inroutine screening of asymptomatic patients.

                  TABLE XIV                                                       ______________________________________                                        Stratification of patients by various means                                   establishes a gradient of risk that is used to validate                       a test such as F-actin.                                                              Patient Stratification Criteria                                                                 F-Actin                                              Patient           QFIA      Prev.  Content                                    Group    Hematuria                                                                              Cytology  Blad. Can.                                                                           Abnormal (%)                               ______________________________________                                        One      --       Positive  --     46(90)                                     Two      Yes      Intermediate                                                                            --     18(75)                                     Three    Yes      Negative  Yes    34(66)                                     Four     Yes      Negative  No     13(36)                                     Five (Control)                                                                         No       Negative  No     3(7)                                       ______________________________________                                    

Establishment of criteria for "positive" and "negative" for markermeasurements in clinical samples is a complex process involving weighingof needs for sensitivity against those for specificity. FIGS. 3 and 21illustrate how this can be achieved for the p300 tumor related antigenwith voided urines and bladder washes respectively. These plots, whichare derived from considerations of information theory and were firstapplied to signal/noise problems in radio, are referred to as "receiveroperating characteristic" (ROC) plots. These plots were derived fromstudies of patients known to have cancer (sensitivity line) and frompatients known not to have a clinically detectable cancer (specificityline). Included in those without disease are patients who aresymptomatic. These plots are examined to determine the optimal thresholdfor identification of abnormal samples. The same plot can be used to setdifferent thresholds as might be used for different applications. Forexample, a different threshold might be applied to populations ofsymptomatic individuals as compared to asymptomatic ones, or when themarker is used alone versus in combination with other markers.

EXAMPLE 5 Risk Assessment Using DNA, M344 and G-Actin as Markers

Multiple marker profiles can be developed to provide an estimate of therisk for cancer faced by an individual patient that can be used to guidesubsequent treatment. This approach was used to stratify a population ofindividuals which had been exposed to a bladder carcinogen in the workplace. The results were used to determine a set of risk categories,shown in Table XV, for guiding the continued clinical followup andmanagement of this group. The three markers used were: DNA as measuredby the % of cells in a urine sample with >5C DNA (ER5C) in the nucleus,the number of cells positive for M344 antibody per 10,000 cells assayed,and the mean G-actin content of the cells.

Samples having an ER5C ≧0.8% of cells assayed were designated as"positive" (+) for ER5C. Samples having an ER5C ≧2.0% of cells weredesignated as "strongly positive" (++). Samples having an ER5C <0.8% ofcells assayed were designated as "negative" (-).

Samples in which either (1) the mean level of G-actin was ≧90 units or(2) the mean level plus the standard deviation of G-actin level was ≧130units were designated as "positive" (+). Samples in which the mean levelof G-actin alone was ≧140 units were designated as "strongly positive"(++) for G-actin. Samples in which mean G-actin was <90 units weredesignated as "negative" (-) for G-actin.

Samples having ≧2 cells which were positive for M344 per 10,000 cellsmeasured were designated as "positive" (+) for ER5C. Samples having ≧10cells per 10,000 measured were designated as "strongly positive" (++).Samples in which there were <2 cells positive for M344 per 10,000 cellsassayed were designated as "negative" (-).

Table XV shows the risk categories (from Very High Risk to High Risk toModerate Risk to Low Risk) correlated with particular profiles of eachof the three markers. Each sample surveyed has a particular markerprofile, and based on the marker profile, is assigned a risk category.Each risk category can be assigned a specific course of clinical action.This course of clinical action can then be proposed to the individualfrom whom the sample was obtained.

For bladder cancer risk, for example, the following protocols may beproposed. Persons assigned to the Very High Risk status may be proposedto follow a course of immediate clinical action which includes acystoscopic examination for tumor and a biopsy, and other diagnostictests as needed. Persons assigned to the High Risk status may beproposed to follow a course of clinical action which includes beinggiven a cystoscopic examination for tumor and having new samples takenand new marker profiles determined at six month intervals. Personsassigned to the Moderate Risk status may be proposed to follow a courseof clinical action which includes having new samples taken and newmarker profiles determined at one-year intervals. Persons assigned toLow Risk status (negative for all three markers) may be proposed tofollow a course of clinical action which includes having new samplestaken and new marker profiles determined at three-year intervals. It caneasily be seen how such risk categories could be expanded to includeadditional markers.

Additionally, the risk categories can be modified or stratified based onadditional personal data. This additional data may comprise demographicinformation such as gender, smoking/non-smoking status, and may includelevel of exposure to the carcinogen. Other known risk factors which maybe known to relate to certain organ sites may be included in the riskprofile. In the case of colon cancer, additional risk factors arepresence of polyps, multiple polyposis and a history of previous cancer.In the case of bladder cancer, additional factors may be diverticulum orurinary stasis.

Moreover, each biomarker has several variables which may influence itsexpression. The first variable is the variability within the test. Asecond variable is a threshold cut-off established by a given markerbased on ROC plots. A third variable is the threshold of intensity offluorescence for the marker.

                  TABLE XV                                                        ______________________________________                                        Bladder Cancer Risk Categories Using DNA, G-actin,                            and M-344 as Markers                                                          Risk Level  ER5C        G-actin M-344                                         ______________________________________                                        Very High Risk                                                                            + or ++     + or ++ + or ++                                                   + or ++     -       ++                                                        -           + or ++ ++                                                        -           -       ++                                            High Risk   +           +       -                                                         ++          -       -                                                         +           -       +                                                         -           ++      -                                                         -           +       +                                                         -           -       +                                             Moderate Risk                                                                             +           -       -                                                         -           +       -                                             Low Risk    -           -       -                                             ______________________________________                                         Legend:                                                                       ER5C:                                                                         - = % of cells having >5C is ≧0.8%                                     + = % of cells having >5C is ≦ 0.8%                                    ++ = % of cells having >5C is ≧ 2.0%                                   Gactin:                                                                       - = mean Gactin level <90 units                                               + = mean Gactin level ≧90 units or mean + S.D. >130 units              ++ = mean Gactin ≧140 units                                            + = <2 cells per 10,000 are positive for M344                                 + = ≧2 cells per 10,000 are positive for M344                          ++ = ≧10 cells per 10,000 are positive for M344                   

EXAMPLE 6 Neural Networks

This example relates to directly encoding gray level images forclassification by neural nets for use in automated cancer diagnosis orin detection of abnormal cells resulting from the process ofcarcinogenesis.

Neural networks are artificial intelligence systems which seek to modelthe physiological process at a neuronal level. The neural networkencodes human-made decisions in the form of connection weights among thecomputational elements. Neural networks are adaptable to circumstancesin which a human is able to consistently make a decision, and does notrequire any encoding of that knowledge, i.e., the neural network can betrained to replicate the human decision without any explicit knowledgeof how that decision was made.

The neural network is a device for "learning." If presented with a setof complex patterns that are classified by a human observer, the neuralnetwork can "learn" to recognize members of each of the classes it was"trained" to recognize. In addition, if new objects that the neural nethas not been trained to recognize are presented, the neural network iscapable of determining that such objects do not fit any currentclassification. Neural networks are, in general, tolerant of somevariation in characteristics within a given class. Neural networks arealso rapid, usually achieving at least a near real time performance,even with complex images.

Though the concept of using an artificial neural network system (ANNS)to solve pattern recognition has been proposed since the fifties, recentadvancement of learning theory and adaptive signal processing hasgreatly increased and strengthened the use of ANNS for practicalproblems. Research projects directed to applications of artificialneural networks can be roughly categorized into three main areas:pattern recognition/associative memory, artificial intelligence, andoptimization. Pattern recognition problems have direct applications inrobotics, machine vision, and natural language understanding. Artificialintelligence problems include game theory, and other heuristicallyoriented applications. Optimization problems include modelling,estimation, prediction, and control. One application of neural networksis in recognition of visual images, such as is required in the presentinvention.

The use of a neural-like network offers several advantages. First, aneural-like network performs the necessary and suitable "transformation"and "clustering" operations automatically and simultaneously. That is,the neural-network is able to abstract the distinctions between normaland abnormal patterns during the training session even though suchdistinctions may not be apparent. Secondly, a multi-layered neural-likenetwork, specifically three or more, is able to recognize complex andnon-linear groups in the hyperspace. This is a distinct advantage overconventional techniques. Thirdly, a neural-like network is massivelyparallel in nature and operates in parallel in close to real-time speed.

In the present invention, neural networks are used as part of anautomated system wherein encoded grey level images are utilized incancer diagnosis and cancer risk assessment.

An ANNS is a network that is composed of a large number of neuron-likeprocessing elements called synthetic neurons that are denselyinterconnected to one another wherein the nodes of the networkrepresenting the synthetic neurons themselves. Used herein, the termneuron refers to synthetic neurons or neuron-like elements. Theoperation of a synthetic neuron can be functionally represented as alinear combiner as shown below (Eq. 9): ##EQU9## where a_(i) is theoutput of the ith neuron in a network of P neurons; W_(ji) is amultiplicative weight representing the synaptic efficacy from the outputof the jth neuron to the input of the ith neuron; s₁ is the intermediatesum of neuronal inputs; and T is the output transformation. Typically,the number of neuronal inputs is large indicating a rich and fullyconnected interconnection network among the neurons. The inputs to aneuron may be real valued or binary valued in general.

Eq. 9 shows that each neuronal input is weighted according to theefficacy of that corresponding junction or synapse. The weighted sum ofall the inputs represents the intermediate output of the neuron. Theactual output of the neuron, however, is a nonlinear function of thisintermediate sum. It is this output transformation that gives the neuronnon-linear characteristics and hence the ability to solve manyoptimization problems. In "first-generation" neural networks, T issigmoid, but other mathematical relationships are not only possible butfrequently offer enhanced performance in terms of improved trainabilityand training times, fewer layers or nodes needed to solve a givenproblem. Examples include sinusoidal or gaussian functions.

An ANNS consists of a collection of such neurons in a fully connectednetwork. Such a network is completely characterized by the state of theneurons and the interconnections. The state of the neurons is usuallyrepresented by a vector describing the outputs of the neurons at aparticular point in time. In addition, the synaptic weights between theneurons are described by a matrix. Information is stored in theinterconnection network and in the efficacy of each synaptic junction,i.e., the synaptic weights. Information processing can be considered tooccur as inputs passed through layers and layers of neural-likeelements. Each layer of neurons provides an added transformation on theinput data. Given an input vector then, the output vector can be derivedfrom the neuronal state vector and the interconnection matrix to producea stable output vector if the dynamics of the neuronal adaptation isknown.

The ANNS can be trained to behave in a specified way by adaptivelychanging the weights toward the direction of the desired goal. There aretwo different approaches to adjusting the weights in a network. Thefirst approach, called supervised learning, involves calculating theerror in output achieved with a particular configuration and using oneof several algorithms to feed that error back into the network. The mostcommonly used rule for changing weights is the generalized delta rule,which is an iterative gradient algorithm designed to minimize the meansquare error between the actual output of a multilayer feed-forwardperceptron and the desired output. This is a more generalized definitionof back propagation employed with a sigmoid output transformationfunction. The mathematics will be different for each T in Eq. 9, becauseeach T must be a continuous, differentiable non-linear function, but theprinciples are similar. This back error propagation approach has beensuccessfully applied to many signature recognition projects such assonar target recognition.

The second approach, which might be defined as a "second-generation"neural network, is ARTMAP, which is an example of unsupervised learning.The theoretical basis for this approach is Adaptive Resonance Theory.The fundamental principle of this theory is that a resonance will beestablished when the weights provide a solution to discriminating aparticular set of patterns. At a conceptual level, this is analogous toan harmonic resonance with standing waves. Such networks arefundamentally different from those using a supervised learning algorithmbecause they are self-organized. ART systems carry out hypothesistesting to discover and learn good recognition codes when given atraining set without common members in exclusive classes. ARTMAP canreject unfamiliar input as being unfamiliar (i.e. it provides an answerof "none of the above") so that incorrect classifications will notoccur.

In this example, an image analysis system was used to performpreliminary algorithmic classification of images of exfoliated urinecells stained with a fluorescent dye which preferentially labels DNA andthen to capture the grey-level images of potentially abnormal cells foranalysis by a neural network. Digitized, grey-level cell images werecaptured on disks and were minimally processed for analysis by a trainedneural network implemented on a Prime computer (VAX 780 equivalent).Minimally processed means normalized such that differences in DNAcontent were not a factor in the decision. The network consisted of aninput layer (1936 neurodes), two hidden layers (50 and 24 neurodes each)and a single output node.

The network was trained with an image set consisting of both low andhigh grade cancer cells and several different examples of noncancercells. A second, test set was evaluated without disagreement with ahuman expert (Table XVI). The principle was proven unequivocally thatgrey level images of cells having DNA labeled with fluorescent probescould serve as direct input for a neural network and that such networkscould be trained to differentiate cancer cells from noncancer cells. Theresults clearly demonstrated the feasibility of using neural networks torecognize and classify grey level images captured by an image analysismicroscope.

In a system for evaluating more than one marker, an image library foreach marker would be selected. This is based upon the assumption thatmarkers do not interfere with each other, an assumption that issatisfied using the processing techniques described herein for thepresent invention.

Methodology

Image library: An image library was collected by an expert cytologistwith a Zeiss IBAS system. The IBAS is a full-function image analysissystem with image capture capabilities. The cells were labeled withH-33258, a fluorescent dye that labels DNA preferentially. The imageswere stored as 512×512 pixel gray level images on floppy disks and sentfor preliminary image processing to extract the image of the cell in a64×64 format. These images were then analyzed at another site. Anadequate image library is selected from cell images from patients knownto have certain clinical conditions. Thus, cells representing cancer aretaken from cells found in the urine of patients known to have cancer.Cells representing the normal condition are taken from individualshaving no indications of cancer. Cells representing premalignantconditions are taken from patents who previously had a cancer but who donot currently have a detectable cancer, and who have one or moreabnormal quantitative markers. Cells representing false results areobtained from patients being seen for urologic conditions other thancancer (e.g., chronic infection or benign prostatic hyperplasia) whohave been evaluated and found to be free of cancer. The cells are thencharacterized by an expert cytologist (e.g., normal cell). Thus, eachimage will have associated two characteristics, its intrinsicclassification and the clinical condition of the patient. An examplemight be "normal cell from cancer patient."

The image library consisted of the following cell types:

Normal cells from noncancer urines: Squamous cells (12 images). Theseare often found in female urine and represent cells that aredifferentiating along a more skin cell-like pathway. Transitionalurothelial cells (12 images) represent the usual cells lining thebladder. Cancers are generally derived from this cell type.Polymorphonuclear leukocytes PML (8 images) are white cells found in theurine as a response to infection or inflammation.

Abnormal cells from cancer cases: Mild-moderate atypical cells (12images) express mild morphological changes. These can be derived fromboth low-grade cancers and noncancer causes, and the challenge ofbladder cancer diagnosis is to identify those that are derived fromcancer. Moderately-severely atypical cells (12 images) are more severelyaltered. Suspicious cells (16 images) are found in high-grade tumors andhave a characteristic abnormal appearance.

A selected image library was prepared as follows. Normal: Squamous cells(10 images), Transitional cells (2 images), PML (1 image); Cancer cases:Suspicious cells (10 images).

Neural Network Training and Testing: The neural network consisted of a1936 neurode input layer (44×44+1 threshold modifier), two hidden layersof 50 (49+threshold modifier) and 24 neurodes respectively and a singleoutput (Abnormal cell vs Normal cell). There was full connectivity, andneurodes were defined as perceptrons. The neural network was simulatedin software on a Prime (VAX 780 equivalent 12 mips machine). Theremainder of the normal cells and suspicious cells were used as a testset.

Results

The training time was approximately 24 hours. The time to classify atest image, however, was only 2 minutes of calculation time. Table XVIdemonstrates the concordance between the human expert and the trainedneural network with the second, independent test set. All test cellimages were correctly classified as either normal cell or abnormal cell.It is of interest that this was achieved with a training set thatemphasized squamous cells over transitional cells, while the test setwas richer in transitional cells. This demonstrates that the resultsachieved with the neural net may have a degree of generalizability.

                  TABLE XVI                                                       ______________________________________                                        Comparison of Cell Classifications by Trained Neural                          Network Versus Human Expert                                                            Classification By                                                                       Classification Using Neural Net                            Cell Type  Human Expert                                                                              Normal     Abnormal                                    ______________________________________                                        Normal Squamous                                                                          2           2          0                                           Cells                                                                         Normal Transitional                                                                      10          10         0                                           Cells                                                                         Normal PMLs                                                                              7           7          0                                           Suspicious 6           0          6                                           Transitional Cells                                                            ______________________________________                                    

A truly improved hybrid system will use algorithmic processing toidentify potentially abnormal objects and a neural network to at leastpartially replace the human in classification of cell images. Hardwareimplementations of neural networks are commercially available. Hardwareimplementation will permit the true massive parallel processing ratherthan software simulation in a linear sequentially-processing digitalcomputer. The approximate 10 μs required to process an image withhardware implementation will make classification of each image feasible.A reduction in training times can be achieved by using a more powerfulcomputer, by various image-compression techniques to reduce the numberof elements in the image, or by a combination of approaches.

EXAMPLE 7 Classification of Bladder Cells Using a Hybrid Multi-LayerNeural Network

Described herein is a new multi-threshold modified Perceptron capable ofhandling both binary and analog input. The modified Perceptron replacesthe sigmoid function with sinusoidal function. A computer program wasdeveloped to simulate behavior of a network utilizing the modifiedPerceptron. A network utilizing this modified Perceptron requires fewernumber of iterations to converge to a solution than that of amulti-layer Perceptron network using back propagation. A hybridmulti-layer network using the modified and sigmoidal perceptron was usedto classify images of bladder cells. The results indicated that thehybrid network was capable of correctly classifying the images.

The single-layer Perceptron was one of the first neural networksdeveloped. It is capable of handling both binary and analog inputs. Thesingle-layer Perceptron can only classify input patterns that can becompletely separated by a single hyperplane. Therefore, problems inwhich the input patterns cannot be separated by a single hyperplane cannot be solved using single-layer Perceptron. This limits utility ofsingle-layer Perceptron and points to the use of multi-layer Perceptronnetworks.

The multi-layer Perceptron network is a feed-forward network with one ormore hidden layers of neurons between the input and output layers. Usingthis architecture, many shortcomings of the single-layer Perceptron canbe avoided. However, because of the added complexity, the convergencetheorem and weight adjustment procedure are not applicable. An alternateprocedure called Back Propagation (BP) was developed (for example, seeD. E. Rumelhart, J. L. McClelland and The PDP Research Group, ParallelDistributed Processing Explorations in the Microstructures of Cognition,Vol. 1. Foundations, MIT Press, Cambridge, Mass,, 1988). This procedureis effective and allows for efficient use of multi-layer Perceptrons.But the procedure does not guarantee convergence to the global minima atall times. Also, it requires a large number of training iterations inorder to learn a given set of transformations.

A modified Perceptron is discussed here. The modified Perceptron is amultiple threshold Perceptron. This Perceptron is capable of handlingboth binary and analog inputs and requires fewer number of iterations(as compared to BP) to develop appropriate input to outputtransformations.

Multi-Layer Perceptron

When the input patterns are not linearly separable a more complexstructure (compared to single-layer Perceptron) is required to classifythese patterns correctly. Multi-layer Perceptron presents one suchstructure. Multi-layer Perceptrons are feed-forward networks with one ormore neuron layers (hidden layers) between the input and the outputnodes.

Because the convergence theorem and weight adjustment proceduresdeveloped for single-layer Perceptron do not apply to multi-layernetworks they had limited utility until a new procedure called BackPropagation (BP) was developed.

The BP approach has proven to be an effective training algorithm howeverit is not guaranteed to converge to the global minima in every instance.Also, BP requires a large number of iterations before it is able toutilize the learned patterns for solving a problem. The utility of theBP technique is due to the surprising computational power of networkswith hidden layers.

Alternate Non-Linearity Function

The non-linearity function of interest not only must be able to formmultiple decision boundaries but also must be continuous and itsderivative must exist for all input patterns if it is to be used in amulti-layer configuration utilizing BP.

There are a number of functions that satisfy the multiple decisionboundaries requirement specified. Sinusoid, Gaussian, double Sigmoid,and some piecewise linear functions all satisfy this requirement. Thedouble sigmoid function correctly forms the required decision boundariesbut it provides three distinct output values (-1,0,1). Therefore, doublesigmoid function can be used to simulate tri-state logic. However, tosimulate a binary logic element, an element capable of providing binaryoutput is required. The same is true for piecewise linear function; inaddition, it cannot be used for multi-layer configurations utilizing BPbecause its derivative is not defined for all values of input.

The Gaussian and sinusoid functions present the best choices for abinary valued neuron. This is due to the fact that their derivatives aredefined for any possible value of input; and they can form multipledecision boundaries. However, the Gaussian function is more difficult towork with and is not as efficient as sinusoidal function. This is due tothe fact that in the present case, it is required to evaluate thederivative of the nonlinearity function used. Also, in order to obtainmore than two decision boundaries, a number of gaussian functions withdifferent mean values must be added. But the periodical nature of thesinusoidal function automatically provides for having any number ofdecision boundaries. Hence, the sinusoidal function only is considered.

Sinusoidal Perceptron

A sinusoidal version of a single-layer Perceptron was developed, inplace of the sigmoid function used by Rosenblatt. The sinusoidalfunction, f_(s) is defined in Eq. 10.

    f.sub.s =Sin(f*a)                                          (10)

where "f" is the frequency and "a" is the weighted sum of the Perceptroninputs.

After each iteration the weights are adjusted as follows:

    w(t)+h(d-y(t)×(t) is slope of f.sub.a ≧0

    w(t+1)=w(t)-h(d-y(t)×(t) is slope of f.sub.s ≦0 (11)

In Eq. 11 w(t) is the weight at iteration "t", h is a positive constantless than "1" called learning rate, and x(t) is the input pattern ofinterest. Variable "d" represents the desired output corresponding tox(t), and y(t) is equal to a_(i) using Eq. 9.

A computer program was developed to simulate the behavior of themodified Perceptron. Results of the simulations indicate that use of asinusoidal function as the non-linearity function allows thesingle-layer Perceptron network to develop all the decision boundaries(hyperplanes) needed to correctly classify more than two distinctclasses of input patterns.

Use in Cancer Cell Classification

The modified perceptron was combined with the traditional perceptron toform a more powerful multi-layer network to analyze images labelled withthe M344 antibody. This network consisted of an input layer, an outputlayer and one hidden layer. The hidden layer and the output layerneurons used the sinusoidal and sigmoid nonlinearity functions,respectively. The network was trained using two different sets oftraining data obtained from the recorded images. The recorded imageswere up to 70×70 pixels.

The first approach utilized a 10×10 set of pixels from the center ofeach cell. Therefore, the network required 100 input neurons. Hiddenlayer had 10 sinusoidal neurons and the output layer had one sigmoidalneuron. A total of 60 training sets was used to train the network todistinguish between cells falsely positive for the M344 marker andnegative cells. A total of four test cases were used. The fourrepresented two positive cells, one negative cell, and one falsepositive cell. The network required an average of 195 iterations tolearn the data. The average number of iterations (maximum of 870 andminimum of 57 iterations) corresponds to different seed numbers used forthe random number generator. The network successfully classified thefour test cases. This approach was not able to correctly learn four ofthe images used for the training set.

The second approach used 55 images for training and 8 for testing thenetwork. The test set consisted of 4 positive, 2 negative, and 2 falsepositive. Each image was reduced to 60×60 pixels.

The network used was a multi-layer network consisting of an input layer(3600 neurons), hidden layer (85 sinusoidal neurons), and an output (onesigmoidal neuron) layer. The network required 503 iterations (between 8to 24 hrs of CPU time depending on the load on the machine) to learn thetraining set. Given an appropriate threshold (0.35 and 0.75) the networkclassified all test cells correctly.

Based on the results obtained from comparing a single-layer network ofthe modified Perceptron and multi-layer Perceptron network using BP, itis shown that the modified Perceptron is more efficient in formingrequired transformations from input to the output during the learningprocess for problems where convex decision regions can be used.

Changes may be made in the embodiments of the invention described hereinor in parts of the elements of the embodiments described herein or inthe steps or in the sequence of steps of the methods described hereinwithout departing from the spirit and scope of the invention as definedin the following claims.

What is claimed is:
 1. A method of analyzing a cell sample derived fromurine or from a bladder wash, comprising:providing a prepared slide, theprepared slide having been prepared by applying a portion of a cellsample to a slide, the portion of the cell sample treated with afixative composition comprising a salt of ethylenediaminetetraaceticacid effective in inhibiting formation of substantially all of thecrystals in the cell sample prior to application of the portion of thecell sample to the slide leaving the prepared slide substantially freeof crystals for improving microscopic analysis of the cell on theprepared slide, then treating the slide with a fluorescent label forlabeling the cytological marker to form a labeled cytological marker;irradiating a portion of the prepared slide with an amount of anexcitation wavelength of light effective in causing the fluorescentlabel in a cell to emit fluorescent light having an emission wavelengthfor forming a field image; using a microscope means to select cellimages on the field image; obtaining a number related to the selectedcell images; and outputting the number for use in classifying the cellsample.
 2. The method of claim 1 wherein in the step of providing aprepared slide the cytological marker is selected from the groupconsisting of DNA, p300, actin, EGFR and HER-2/neu protein.
 3. Themethod of claim 1 wherein in the step of providing a prepared slide thefluorescent label is comprised of a fluorochrome bound to an affinityprobe.
 4. The method of claim 3 wherein in the step of providing aprepared slide the fluorochrome is selected from the group consisting ofTexas Red, bodipy and fluorescein.
 5. The method of claim 1 wherein inthe step of providing a prepared slide the fluorescent label iscomprised of a dye specific for DNA.
 6. The method of claim 5 wherein inthe step of providing a prepared slide the fluorescent label is Hoechst33258.
 7. The method of claim 1 wherein the step of using a microscopemeans is preceded by the step of correcting the field image forautofluorescence.
 8. The method of claim 1 wherein the step of obtaininga number is preceded by the step of reviewing the selected cell imageswith a confirmation means confirming that the selected cell imagesrepresent cells of a desired type.
 9. The method of claim 8 wherein inthe step of reviewing the selected cell images the confirmation means isa neural net computing means.
 10. The method of claim 1 wherein the stepof outputting the number is followed by the step of classifying the cellsample.
 11. The method of claim 1 wherein in the step of providing aprepared slide, the prepared slide is further defined as having beenprepared by treating the slide with a second fluorescent label forlabeling a second cytological marker to form a labeled secondcytological marker.
 12. The method of claim 11 further comprising theadditional step of analyzing the prepared slide for the secondcytological marker comprising:irradiating a second portion of theprepared slide with an amount of a second excitation wavelength of lighteffective in causing the cells containing the second fluorescent labelto emit fluorescent light having a second emission wavelength forforming a second field image wherein the second portion may be the sameas the first portion, using the microscope means to select second cellimages on the second field image, obtaining a second number related tothe selected second cell images, and outputting the second number foruse in classifying the cell sample.
 13. The method of claim 12 whereinthe step of using the microscope means to select second cell images ispreceded by the step of correcting the second field image forautofluorescence.
 14. The method of claim 12 wherein the step ofobtaining a second number is preceded by the step of reviewing theselected second cell images with a confirmation means for confirmingthat the selected second cell images represent cells of a desired type.15. The method of claim 14 wherein in the step of reviewing the selectedsecond cell images the confirmation means is a neural net computingmeans.
 16. The method of claim 12 wherein the step of outputting thesecond number is followed by the step of using the first number and thesecond number to classify the cell sample.
 17. The method of claim 11wherein in the step of providing a prepared slide, the prepared slide isfurther defined as having been prepared by treating the slide with athird fluorescent label for labeling a third cytological marker to forma labeled third cytological marker.
 18. The method of claim 17 furthercomprising the additional step of analyzing the prepared slide for thethird cytological marker comprising:irradiating a third portion of theprepared slide with an amount of a third excitation wavelength of lighteffective in causing the cells containing the third fluorescent label toemit fluorescent light having a third emission wavelength for forming athird field image, wherein the third portion may be the same as thesecond portion or the first portion, using the microscope means toselect third cell images on the field third image, obtaining a thirdnumber related to the selected third cell images, and outputting thethird number for use in classifying the cell sample.
 19. The method ofclaim 18 wherein the step of using the microscope means to select thirdcell images is preceded by the step of correcting the third field imagefor autofluorescence.
 20. The method of claim 18 wherein the step ofobtaining a third number is preceded by the step of reviewing theselected third cell images with a confirmation means for confirming thatthe selected third cell images represent cells of a desired type. 21.The method of claim 20 wherein in the step of reviewing the selectedthird cell images the confirmation means is a neural net computingmeans.
 22. The method of claim 18 wherein the step of outputting thethird number is followed by the step of using the first number, thesecond number and the third number to classify the cell sample.
 23. Amethod of analyzing a cell sample derived from urine or from a bladderwash, comprising:providing a prepared slide, the prepared slide havingbeen prepared by applying a portion of a cell sample to a slide, theportion of the cell sample treated with a fixative compositioncomprising a salt of ethylenediaminetetraacetic acid effective ininhibiting formation of substantially all of the crystals in the cellsample prior to application of the portion of the cell sample to theslide leaving the prepared slide substantially free of crystals forimproving microscopic analysis of the cell on the prepared slide, thentreating the slide with a first fluorescent label for labeling the firstcytological marker to form a labeled first cytological marker and thesecond fluorescent label for labeling the second cytological marker toform a labeled second cytological marker; irradiating a first portion ofthe prepared slide with an amount of a first excitation wavelength oflight effective in causing the first fluorescent label in the cell toemit fluorescent light having a first emission wavelength for forming afirst field image; using a microscope means to select first cell imageson the first field image; obtaining a first number related to theselected first cell images; irradiating the second portion of theprepared slide with a second excitation wavelength of light effective incausing the second fluorescent label to emit fluorescent light having asecond emission wavelength for forming a second field image wherein thesecond portion may be the same as the first portion; using themicroscope means to select second cell images on the second field image;obtaining a second number related to the selected second cell images;and outputting the first number and the second number for use inclassifying the cell sample.
 24. The method of claim 23 wherein the stepof using a microscope means to select first cell images is preceded bythe step of correcting the first field image for autofluorescence. 25.The method of claim 23 wherein the step of obtaining a first number ispreceded by the step of reviewing the selected first cell images with aconfirmation means for confirming that the selected cell imagesrepresent cells of a desired type.
 26. The method of claim 25 wherein inthe step of reviewing the selected first cell images the confirmationmeans is a neural net computing means.
 27. The method of claim 23wherein the step of obtaining a second number is preceded by the step ofreviewing the selected second cell images with a confirmation means forconfirming that the selected second cell images represent cells of adesired type.
 28. The method of claim 27 wherein in the step ofreviewing the selected second cell images the confirmation means is aneural net computing means.
 29. The method of claim 23 wherein in thestep of providing a prepared slide the first cytological marker and thesecond cytological marker are selected from the group consisting of DNA,p300, actin, EGFR and HER-2/neu protein.
 30. The method of claims 23wherein in the step of providing a prepared slide the first fluorescentlabel and the second fluorescent label are selected from the groupconsisting of Hoechst 33258, M344 plus a fluorescent conjugate, ananti-HER-2/neu protein probe plus a fluorescent conjugate, an anti-EGFRprobe plus a fluorescent conjugate, and DNase I plus a fluorescentconjugate.
 31. The method of claim 30 wherein in the step of providing aprepared slide the fluorescent conjugate is selected from the groupconsisting of Texas Red, bodipy and fluorescein.
 32. The method of claim23 wherein the first cytological marker is DNA and the secondcytological marker is the p300 protein antigen.
 33. The method of claim23 wherein in the step of providing a prepared slide the firstcytological marker is DNA and the second cytological markers is actin.34. The method of claim 23 wherein the step of outputting the firstnumber and the second number is followed by the step of classifying thecell sample.
 35. The method of claim 23 wherein in the step of providinga prepared slide, the prepared slide is further defined as having beenprepared by treating the slide with a third fluorescent label forlabeling a third cytological marker to form a labeled third cytologicalmarker.
 36. The method of claim 35 further comprising the additionalstep of analyzing the prepared slide for the third cytological markercomprising:irradiating a third portion of the prepared slide with anamount of a third excitation wavelength of light effective in causingthe cells containing the third fluorescent label to emit fluorescentlight having a third emission wavelength for forming a third field imagewherein the third portion may be the same as the second portion or thefirst portion, using the microscope means to select third cell images onthe third field image, obtaining a third number related to the selectedthird cell images, and outputting the third number for use inclassifying the cell sample.
 37. The method of claim 36 wherein the stepof using the microscope means to select third cell images is preceded bythe step of correcting the third field image for autofluorescence. 38.The method of claim 36 wherein the step of obtaining a third number ispreceded by the step of reviewing the selected third cell images with aconfirmation means for confirming that the selected third cell imagesrepresent cells of a desired type.
 39. The method of claim 38 wherein inthe step of reviewing the selected third cell images the confirmationmeans is a neural net computing means.
 40. The method of claim 36wherein the step of outputting the third number is followed by the stepof classifying the cell sample.
 41. A method of analyzing a cell samplederived from urine or from a bladder wash, comprising:providing aprepared slide, the prepared slide having been prepared by applying aportion of a cell sample to a slide, the portion of the cell sampletreated with a fixative composition comprising a salt ofethylenediaminetetraacetic acid effective in inhibiting formation ofsubstantially all of the crystals in the cell sample prior toapplication of the portion of the cell sample to the slide leaving theprepared slide substantially free of crystals for improving microscopicanalysis of the cell on the prepared slide, then treating the slide witha first fluorescent label for labeling the first cytological marker toform a labeled first cytological marker, a second fluorescent label forlabeling the second cytological marker to form a labeled secondcytological marker, and a third fluorescent label for labeling the thirdcytological marker to form a labeled third cytological marker;irradiating a first portion of the prepared slide with an amount of afirst excitation wavelength of light effective in causing the firstfluorescent label in the cell to emit fluorescent light having a firstemission wavelength for forming a first field image; using a microscopemeans to select first cell images on the first field image; obtaining afirst number related to the selected first cell images; irradiating asecond portion of the prepared slide with a second excitation wavelengthof light effective in causing the second fluorescent label to emitfluorescent light having a second emission wavelength for forming asecond field image visible wherein the second portion may be the same asthe first portion; using the microscope means to select second cellimages on the second field image; obtaining a second number related tothe selected second cell images; irradiating a third portion of theprepared slide with a third excitation wavelength of light effective incausing the third fluorescent label to emit fluorescent light having athird emission wavelength for forming a third field image wherein thethird portion may be the same as the second portion or the firstportion; using the microscope means to select third cell images on thethird field image; obtaining a third number related to the selectedthird cell images; and outputting the first number, the second number,and the third number for use in classifying the cell sample.
 42. Themethod of claim 41 wherein the step of using a microscope means toselect first cell images is preceded by the step of correcting the firstfield image for autofluorescence.
 43. The method of claim 41 wherein thestep of obtaining a first number is preceded by the step of reviewingthe selected first cell images with a confirmation means for confirmingthat the selected cell images represent cells of a desired type.
 44. Themethod of claim 43 wherein in the step of reviewing the selected firstcell images the confirmation means is a neural net computing means. 45.The method of claim 41 wherein the step of using the microscope means toselect second cell images is preceded by the step of correcting thesecond field image for autofluorescence.
 46. The method of claim 41wherein the step of obtaining a second number is preceded by the step ofreviewing the selected second cell images with a confirmation means forconfirming that the selected second cell images represent cells of adesired type.
 47. The method of claim 46 wherein in the step ofreviewing the selected second cell images the confirmation means is aneural net computing means.
 48. The method of claim 41 wherein the stepof using the microscope means to select third cell images is preceded bythe step of correcting the third field image for autofluorescence. 49.The method of claim 41 wherein the step of obtaining a third number ispreceded by the step of reviewing the selected third cell images with aconfirmation means for confirming that the selected third cell imagesrepresent cells of a desired type.
 50. The method of claim 49 wherein inthe step of reviewing the selected third cell images the confirmationmeans is a neural net computing means.
 51. The method of claim 41wherein in the step of providing a prepared slide the first cytologicalmarker, the second cytological marker and the third cytological markerare selected from the group consisting of DNA, p300, actin, EGFR andHER-2/neu protein.
 52. The method of claim 41 wherein in the step ofproviding a prepared slide the first fluorescent label, the secondfluorescent label and the third fluorescent label are selected from thegroup consisting of Hoechst 33258, M344 plus a fluorescent conjugate, ananti-HER-2/neu protein probe plus a fluorescent conjugate, an anti-EGFRprobe plus a fluorescent conjugate, and DNase I plus a fluorescentconjugate.
 53. The method of claim 52 wherein in the step of providing aprepared slide the fluorescent conjugate is selected from the groupTexas Red, bodipy and fluorescein.
 54. The method of claim 41 wherein inthe step of providing a prepared slide the first cytological marker isDNA and the second cytological marker is selected from the groupconsisting of p300, actin, EGFR and HER-2/neu protein.
 55. The method ofclaim 41 wherein in the step of providing a prepared slide the firstcytological marker is DNA, the second cytological marker is the p300protein antigen and the third cytological marker is selected from thegroup consisting of EGFR, actin and HER-2/neu protein.
 56. The method ofclaim 41 wherein in the step of providing a prepared slide the firstcytological marker is DNA, the second cytological marker is actin andthe third cytological marker is selected from the group consisting ofp300, EGFR and HER-2/neu protein.
 57. The method of claim 41 wherein thestep of outputting the first number, the second number and the thirdnumber is followed by the step of classifying the cell sample.
 58. Amethod of analyzing a cell sample, comprising:providing a preparedslide, the prepared slide having been prepared by applying a portion ofa cell sample to a slide then treating the slide with a fluorescentlabel for labeling a cytological marker to form a labeled cytologicalmarker; irradiating a portion of the prepared slide with an amount of anexcitation wavelength of light effective in causing the fluorescentlabel in the cell to emit fluorescent light having an emissionwavelength for forming a field image; digitizing the field image;converting the field image into a plurality of digitized pixels;obtaining for each pixel of the field image an intensity measurement ofthe peak emission wavelength: correcting the field image forautofluorescence by selecting an off peak emission wavelength, obtainingan intensity measurement of the off peak emission wavelength for eachpixel of the field image, and subtracting the intensity measurement ofthe off peak emission wavelength from the intensity measurement of thepeak emission wavelength for each pixel of the field image wherein isobtained a corrected field image; using a microscope means to selectcell images on the corrected field image; obtaining a number related tothe selected cell images; and outputting the number for use inclassifying the cell sample.
 59. The method of claim 58 wherein in thestep of providing a prepared slide the cytological marker is selectedfrom the group consisting of DNA, p300, actin, EGFR and HER-2/neuprotein.
 60. The method of claim 58 wherein in the step of providing aprepared slide the fluorescent label is comprised of a fluorochromebound to an affinity probe.
 61. The method of claim 60 wherein in thestep of providing a prepared slide the fluorochrome is selected from thegroup consisting of Texas Red, bodipy and fluorescein.
 62. The method ofclaim 58 wherein in the step of providing a prepared slide thefluorescent label is comprised of a dye specific for DNA.
 63. The methodof claim 62 wherein in the step of providing a prepared slide thefluorescent label in Hoechst
 33258. 64. The method of claim 58 whereinthe step of obtaining a number is preceded by the step of reviewing theselected cell images with a confirmation means confirming that theselected cell images represent cells of a desired type.
 65. The methodof claim 64 wherein in the step of reviewing the selected cell imagesthe confirmation means is a neural net computing means.
 66. The methodof claim 58 wherein in the step of providing a prepared slide, theportion of the cell sample used in preparing the slide was fixated usinga fixative composition comprising a salt of ethylenediaminetetraaceticacid effective in inhibiting crystal formation.
 67. The method of claim58 wherein the step of providing a prepared slide, the cell sample iscollected by washing a body organ, collected from a body fluid, orcollected from a needle aspiration of a body gland or organ.
 68. Amethod of analyzing a cell sample, comprising:providing a preparedslide, the prepared slide having been prepared by applying a portion ofa cell sample to a slide then treating the slide with a firstfluorescent label for labeling a first cytological marker to form alabeled first cytological marker and a second fluorescent label forlabeling a second cytological marker to form a labeled secondcytological marker; irradiating a first portion of the prepared slidewith an amount of a first excitation wavelength of light effective incausing the first fluorescent label in the cell to emit fluorescentlight having a first teak emission wavelength for forming a first fieldimage; converting the first field image into a plurality of digitizedpixels; obtaining for each pixel of the first field image an intensitymeasurement of the first peak emission wavelength; correcting the firstfield image for autofluorescence by selecting a first off peak emissionwavelength, obtaining an intensity measurement of the first off peakemission wavelength for each pixel of the first field image, andsubtracting the intensity measurement of the first off peak emissionwavelength from the intensity measurement of the first peak emissionwavelength for each pixel of the first field image wherein is obtained acorrected first field image; using a microscope means to select firstcell images on the corrected first field image; obtaining a first numberrelated to the selected first cell images; irradiating a second portionof the prepared slide with a second excitation wavelength of lighteffective in causing the second fluorescent label to emit fluorescentlight having a second emission wavelength for forming a second fieldimage wherein the second portion may be the same as the first portion;converting the second field image into a plurality of digitized pixels;obtaining for each pixel an intensity measurement of the second peakemission wavelength; correcting the second field image forautofluorescence by selecting a second off peak emission wavelength,obtaining an intensity measurement of the second off peak emissionwavelength for each pixel of the second field image, and subtracting theintensity measurement of the second off peak emission wavelength fromthe intensity measurement of the second peak emission wavelength foreach pixel of the second field image wherein is obtained a correctedsecond field image; using the microscope means to select second cellimages on the corrected second field image; obtaining a second numberrelated to the selected second cell images; and outputting the firstnumber and the second number for use in classifying the cell sample. 69.The method of claim 68 wherein in the step of providing a preparedslide, the portion of the cell sample used in preparing the slide wasfixated using a fixative composition comprising a salt ofethylenediaminetetraacetic acid effective in inhibiting crystalformation.
 70. The method of claim 68 comprising the additional step ofusing the first number and the second number to classify the cellsample.
 71. The method of claim 68 wherein the step of obtaining a firstnumber is preceded by the step of reviewing the selected first cellimages with a confirmation means for confirming that the selected cellimages represent cells of a desired type.
 72. The method of claim 71wherein in the step of reviewing the selected first cell images theconfirmation means is a neural net computing means.
 73. The method ofclaim 68 wherein the step of obtaining a second number is preceded bythe step of reviewing the selected second cell images with aconfirmation means for confirming that the selected second cell imagesrepresent cells of a desired type.
 74. The method of claim 73 wherein inthe step of reviewing the selected second cell images the confirmationmeans is a neural net computing means.
 75. The method of claim 68wherein in the step of providing a prepared slide the first cytologicalmarker and the second cytological marker are selected from the groupconsisting of DNA, p300, actin, EGFR and HER-2/neu protein.
 76. Themethod of claim 68 wherein in the step of providing a prepared slide thefirst fluorescent label and the second fluorescent label are selectedfrom the group consisting of Hoechst 33258, M344 plus a fluorescentconjugate, an anti-HER-2/neu protein probe plus a fluorescent conjugate,an anti-EGFR probe plus a fluorescent conjugate, and DNase I plus afluorescent conjugate.
 77. The method of claim 76 wherein in the step ofproviding a prepared slide the fluorescent conjugate is selected fromthe group consisting of Texas Red, bodipy and fluorescein.
 78. Themethod of claim 68 wherein the first cytological marker is DNA and thesecond cytological marker is the p300 protein antigen.
 79. The method ofclaim 68 wherein in the step of providing a prepared slide the firstcytological marker is DNA and the second cytological marker is actin.80. The method of claim 68 wherein the step of providing a preparedslide, the cell sample is collected by washing a body organ, collectedfrom a body fluid, or collected from a needle aspiration of a body glandor organ.
 81. A method of analyzing a cell sample, comprising:providinga prepared slide, the prepared slide having been prepared by applying aportion of a cell sample to a slide then treating the slide with a firstfluorescent label for labeling a first cytological marker to form alabeled first cytological marker, a second fluorescent label forlabeling a second cytological marker to form a labeled secondcytological marker, and a third fluorescent label for labeling a thirdcytological marker to form a labeled third cytological marker;irradiating a first portion of the prepared slide with an amount of afirst excitation wavelength of light effective in causing the firstfluorescent label in the cell to emit fluorescent light having a firstpeak emission wavelength for forming a first field image; converting thefirst field image into a plurality of digitized pixels; obtaining foreach pixel of the first field image an intensity measurement of thefirst peak emission wavelength; correcting the first field image forautofluorescence by selecting a first off peak emission wavelength,obtaining an intensity measurement of the first off peak emissionwavelength for each pixel of the first field image, and subtracting theintensity measurement of the first off peak emission wavelength from theintensity measurement of the first peak emission wavelength for eachpixel of the first field image wherein is obtained a corrected firstfield image; using a microscope means to select first cell images on thecorrected first field image; obtaining a first number related to theselected first cell images; irradiating a second portion of the preparedslide with a second excitation wavelength of light effective in causingthe second fluorescent label to emit fluorescent light having a secondemission wavelength for forming a second field image wherein the secondportion may be the same as the first portion; converting the secondfield image into a plurality of digitized pixels; obtaining for eachpixel an intensity measurement of the second peak emission wavelength;correcting the second field image for autofluorescence by selecting asecond off peak emission wavelength, obtaining an intensity measurementof the second off peak emission wavelength for each pixel of the secondfield image, and subtracting the intensity measurement of the second offpeak emission wavelength from the intensity measurement of the secondpeak emission wavelength for each pixel of the second field imagewherein is obtained a corrected second field image; using the microscopemeans to select second cell images on the corrected second field image;obtaining a second number related to the selected second cell images;irradiating a third portion of the prepared slide with a thirdexcitation wavelength of light effective in causing the thirdfluorescent label to emit fluorescent light having a third peak emissionwavelength for forming a third field image wherein the third portion maybe the same as the second portion or the first portion; correcting thethird field image for autofluorescence by selecting a third off peakemission wavelength, obtaining an intensity measurement of the third offpeak emission wavelength for each pixel of the third field image, andsubtracting the intensity measurement of the third off peak emissionwavelength from the intensity measurement of the third peak emissionwavelength for each pixel of the third field image wherein is obtained acorrected third field image; using the microscope means to select thirdcell images on the corrected third field image; obtaining a third numberrelated to the selected third cell images; and outputting the firstnumber, the second number, and the third number for use in classifyingthe cell sample.
 82. The method of claim 81 wherein the step ofobtaining a first number is preceded by the step of reviewing theselected first cell images with a confirmation means for confirming thatthe selected first cell images represent cells of a desired type. 83.The method of claim 82 wherein in the step of reviewing the selectedfirst cell images the confirmation means is a neural net computingmeans.
 84. The method of claim 81 wherein the step of obtaining a secondnumber is preceded by the step of reviewing the selected second cellimages with a confirmation means for confirming that the selected secondcell images represent cells of a desired type.
 85. The method of claim84 wherein in the step of reviewing the selected second cell images theconfirmation means is a neural net computing means.
 86. The method ofclaim 81 wherein the step of obtaining a third number is preceded by thestep of reviewing the selected third cell images with a confirmationmeans for confirming that the selected third cell images represent cellsof a desired type.
 87. The method of claim 81 wherein in the step ofreviewing the selected third cell images the confirmation means is aneural net computing means.
 88. The method of claim 81 wherein in thestep of providing a prepared slide, the portion of the cell sample usedin preparing the slide was fixated using a fixative compositioncomprising a salt of ethylenediaminetetraacetic acid effective ininhibiting crystal formation.
 89. The method of claim 81 wherein in thestep of providing a prepared slide the first cytological marker, thesecond cytological marker and the third cytological marker are selectedfrom the group consisting of DNA, p300, actin, EGFR and HER-2/neuprotein.
 90. The method of claim 81 wherein in the step of providing aprepared slide the first fluorescent label, the second fluorescent andthe third fluorescent label are selected from the group consisting ofHoechst 33258, M344 plus a fluorescent conjugate, an anti-HER-2/neuprotein probe plus a fluorescent conjugate, an anti-EGFR probe plus afluorescent conjugate, and DNase I plus a fluorescent conjugate.
 91. Themethod of claim 90 wherein in the step of providing a prepared slide,the fluorescent conjugate is selected from the group Texas Red, bodipyand fluorescein.
 92. The method of claim 81 wherein in the step ofproviding a prepared slide the first cytological marker is DNA and thesecond cytological marker is selected from the group consisting of p300,actin, EGFR and HER-2/neu protein.
 93. The method of claim 81 wherein inthe step of providing a prepared slide the first cytological marker isDNA, the second cytological is the p300 protein antigen and the thirdcytological marker is selected from the group consisting of EGFR, actinand HER-2/neu protein.
 94. The method of claim 81 wherein in the step ofproviding a prepared slide the first cytological marker is DNA, thesecond cytological marker is actin and the third cytological marker isselected from the group consisting of p300, EGFR and HER-2/neu protein.95. The method of claim 81 wherein the step of providing a preparedslide, the cell sample is collected by washing a body organ, collectedfrom a body fluid, or collected from a needle aspiration of a body glandor organ.
 96. The method of claim 81 comprising the additional step ofusing the first number, the second number, and the third number toclassify the cell sample.
 97. A method of analyzing a cell samplederived from urine or from a bladder wash, comprising:providing aprepared slide, the prepared slide having been prepared by applying aportion of a cell sample to a slide then treating the slide with afluorescent label for labeling a cytological marker to form a labeledcytological marker and wherein the portion of the cell sample used inpreparing the slide was fixated using a fixative composition comprisinga salt of ethylenediaminetetraacetic acid effective in inhibitingformation of substantially all of the crystals in the sample;irradiating a portion of the prepared slide with an amount of anexcitation wavelength of light effective in causing the fluorescentlabel in the cell to emit fluorescent light having an emissionwavelength for forming a field image; using a microscope means to selectcell images on the field image; reviewing the selected cell images witha neural net computing means for confirming that the selected cellimages represent cells of a desired type; obtaining a number related tothe selected cell images; and outputting the number for use inclassifying the cell sample.
 98. The method of claim 97 wherein in thestep of providing a prepared slide the cytological marker is selectedfrom the group consisting of DNA, p300, actin, EGFR and HER-2/neuprotein.
 99. The method of claim 97 wherein in the step of providing aprepared slide the fluorescent label is comprised of a fluorochromebound to an affinity probe.
 100. The method of claim 99 wherein in thestep of providing a prepared slide the fluorochrome is selected from thegroup consisting of Texas Red, bodipy and fluorescein.
 101. The methodof claim 97 wherein in the step of providing a prepared slide thefluorescent label is comprised of a dye specific for DNA.
 102. Themethod of claim 101 wherein in the step of providing a prepared slidethe fluorescent label is Hoechst
 33258. 103. The method of claim 97wherein the step of using a microscope means is preceded by the step ofcorrecting the field image for autofluorescence.
 104. The method ofclaim 97 wherein the step of providing a prepared slide, the cell sampleis collected by washing a body organ, collected from a body fluid, orcollected from a needle aspiration of a body gland or organ.
 105. Amethod of analyzing a cell sample, comprising:providing a preparedslide, the prepared slide having been prepared by applying a portion ofa cell sample to a slide then treating the slide with a fluorescentlabel for labeling a cytological marker to form a labeled cytologicalmarker; irradiating a portion of the prepared slide with an amount of anexcitation wavelength of light effective in causing the fluorescentlabel in the cell to emit fluorescent light having a peak emissionwavelength for forming a field image; converting the field image into aplurality of digitized pixels; obtaining for each pixel of the fieldimage an intensity measurement of the peak emission wavelength;correcting the field image for autofluorescence by selecting an off peakemission wavelength, obtaining an intensity measurement of the off peakemission wavelength for each pixel of the field image, and subtractingthe intensity measurement of the off peak emission wavelength from theintensity measurement of the peak emission wavelength for each pixel ofthe field image wherein is obtained a corrected field image; using amicroscope means to select cell images on the corrected field image;classifying a cell image as positive or negative for a predeterminedguantity of the fluorescent label; using a neural net computing means tofurther classify a positive cell image as a true-positive cell image oras a false-positive cell image; obtaining a parameter related to thenumber of true-positive cell images; and outputting the parameter foruse in classifying the cell sample.
 106. The method of claim 105 whereinin the step of providing a prepared slide the cytological marker isselected from the group consisting of DNA, p300, actin, EGFR andHER-2/neu protein.
 107. The method of claim 105 wherein in the step ofproviding a prepared slide the fluorescent label is comprised of afluorochrome bound to an affinity probe.
 108. The method of claim 107wherein in the step of providing a prepared slide the fluorochrome isselected from the group consisting of Texas Red, bodipy and fluorescein.109. The method of claim 107 wherein in the step of providing a preparedslide the fluorescent label is Hoechst
 33258. 110. The method of claim105 wherein in the step of providing a prepared slide the fluorescentlabel is comprised of a dye specific for DNA.
 111. The method of claim110 wherein in the step of providing a prepared slide the fluorescentlabel is Hoechst
 33258. 112. The method of claim 105 wherein in the stepof providing a prepared slide the portion of the cell sample used inpreparing the slide was fixated using a fixative composition comprisinga salt of ethylenediaminetetraacetic acid effective in inhibitingcrystal formation.
 113. The method of claim 105 wherein the step ofproviding a prepared slide, the cell sample is collected by washing abody organ, collected from a body fluid, or collected from a needleaspiration of a body gland or organ.
 114. A method of analyzing a cellsample derived from urine or from a bladder wash, comprising:providing aprepared slide, the prepared slide having been prepared by applying aportion of a cell sample to a slide then treating the slide with afluorescent label for labeling a cytological marker to form a labeledcytological marker and wherein the portion of the cell sample used inpreparing the slide was fixated using a fixative composition comprisinga salt of ethylenediaminetetraacetic acid effective in inhibiting theformation of substantially all of the crystals in the sample;irradiating a portion of the prepared slide with an amount of anexcitation wavelength of light effective in causing the fluorescentlabel in the cell to emit fluorescent light having an emissionwavelength for forming a field image; correcting the field image forautofluorescence; using a microscope means to select cell images on thefield image; obtaining a number related to the selected cell images; andoutputting the number for use in classifying the cell sample.
 115. Themethod of claim 114 wherein in the step of providing a prepared slidethe cytological marker is selected from the group consisting of DNA,p300, actin, EGFR and HER-2/neu protein.
 116. The method of claim 114wherein in the step of providing a prepared slide the fluorescent labelis comprised of a fluorochrome bound to an affinity probe.
 117. Themethod of claim 116 wherein in the step of providing a prepared slidethe fluorochrome is selected from the group consisting of Texas Red,bodipy and fluorescein.
 118. The method of claim 114 wherein in the stepof providing a prepared slide the fluorescent label is comprised of adye specific for DNA.
 119. The method of claim 114 wherein the step ofobtaining a number is preceded by the step of reviewing the selectedcell images with a confirmation means confirming that the selected cellimages represent cells of a desired type.
 120. The method of claim 119wherein in the step of reviewing the selected cell images theconfirmation means is a neural net computing means.
 121. A method ofanalyzing a cell sample derived from urine or from a bladder wash,comprising:providing a prepared slide, the prepared slide having beenprepared by applying a portion of a cell sample to a slide then treatingthe slide with a fluorescent label for labeling a cytological marker toform a labeled cytological marker and wherein the portion of the cellsample used in preparing the slide was fixated using a fixativecomposition comprising a salt of ethylenediaminetetraacetic acideffective in inhibiting the formation of substantially all of thecrystals in the sample; irradiating a portion of the prepared slide withan amount of an excitation wavelength of light effective in causing thefluorescent label in the cell to emit fluorescent light having anemission wavelength for forming a field image; correcting the fieldimage for autofluorescence; using a microscope means to select cellimages on the field image; reviewing the selected cell images with aneural net computing means for confirming that the selected cell imagesrepresent cells of a desired type; obtaining a number related to theselected cell images; and outputting the number for use in classifyingthe cell sample.
 122. The method of claim 121 wherein in the step ofproviding a prepared slide the cytological marker is selected from thegroup consisting of DNA, p300, actin, EGFR and HER-2/neu protein. 123.The method of claim 121 wherein in the step of providing a preparedslide the fluorescent label is comprised of a fluorochrome bound to anaffinity probe.
 124. The method of claim 123 wherein in the step ofproviding a prepared slide the fluorochrome is selected from the groupconsisting of Texas Red, bodipy and fluorescein.
 125. The method ofclaim 121 wherein in the step of providing a prepared slide thefluorescent label is comprised of a dye specific for DNA.
 126. Themethod of claim 125 wherein in the step of providing a prepared slidethe fluorescent label is Hoechst
 33258. 127. A method of assessing anindividual's risk for bladder cancer, comprising:providing a preparedslide having a population of cells affixed thereto, the prepared slidehaving been prepared by applying a portion of a cell sample provided bythe individual to a slide then treating the slide with at least a firstfluorescent label for labeling cellular DNA and at least a secondfluorescent label for labeling cellular p300 protein; irradiating theprepared slide with an amount of a first excitation wavelength of lighteffective in causing the first fluorescent label in the cells on theprepared slide to emit fluorescent light having a first emissionwavelength; obtaining a first population parameter related to the numberof cells in the population of cells having quantities of cellular DNAwhich exceed a predetermined threshold quantity of cellular DNA;irradiating the prepared slide with a second excitation wavelength oflight effective in causing the second fluorescent label in the cells onthe prepared slide to emit fluorescent light having a second emissionwavelength; obtaining a second population parameter related to thenumber of cells in the population of cells having quantities of p300protein which exceed a predetermined threshold quantity of p300 protein;comparing the first population parameter to a set of predetermined firstparameter thresholds; comparing the second population parameter to a setof predetermined second parameter thresholds; and assigning apredetermined risk for bladder cancer to the individual based on whichfirst parameter thresholds and second parameter thresholds are exceeded.128. The method of claim 127 wherein in the step of providing a preparedslide, the portion of the cell sample used in preparing the slide wasfixated using a fixative composition comprising a salt ofethylenediaminetetraacetic acid effective in inhibiting crystalformation.
 129. The method of claim 127 wherein the step of obtaining afirst population parameter is preceded by the step of using confirmationmeans for confirming that the cells labeled with the first fluorescentlabel represent cells of a desired type.
 130. The method of claim 127wherein in the step of providing a prepared slide, the portion of thecell sample used in preparing the slide was a urine or bladder washsample fixated using a fixative composition comprising a salt ofethylene-diaminetetraacetic acid effective in inhibiting crystalformation.
 131. The method of claim 127 wherein the step of obtaining asecond population parameter is preceded by the step of usingconfirmation means for confirming that the cells labeled with the secondfluorescent label represent cells of a desired type.
 132. A method ofanalyzing a cell sample derived from urine or from a bladder wash,comprising:providing a prepared slide, the prepared slide having beenprepared by applying a portion of a cell sample to a slide then treatingthe slide with a fluorescent label for labeling a cytological marker toform a labeled cytological marker and wherein the portion of the cellsample used in preparing the slide was fixated using a fixativecomposition comprising a salt of ethylenediaminetetraacetic acideffective in inhibiting formation of substantially all of the crystalsin the sample; irradiating a portion of the prepared slide with anamount of an excitation wavelength of light effective in causing thefluorescent label in the cell to emit fluorescent light having anemission wavelength for forming a field image; using a microscope meansto select cell images on the field image; classifying the cell image aspositive or negative for the fluorescent label; using a neural netcomputing means to further classify a positive cell image as atrue-positive cell image or as a false-positive cell image; obtaining aparameter related to the number of true-positive cell images; andoutputting the parameter for use in classifying the cell sample. 133.The method of claim 132 wherein the step of using a microscope means toselect cell images is preceded by the step of correcting the field imagefor autofluorescence.
 134. The method of claim 132 wherein the step ofclassifying the cell image is preceded by the step of reviewing theselected cell images with a confirmation means for confirming that theselected cell images represent cells of a desired type.
 135. The methodof claim 132 wherein in the step of providing a prepared slide thecytological marker is selected from the group consisting of DNA, p300,actin, EGFR and HER-2/neu protein.
 136. The method of claim 135 whereinthe cytological marker is the p300 protein antigen.
 137. The method ofclaim 135 wherein in the step of providing a prepared slide thecytological marker is actin.
 138. The method of claim 132 wherein thestep of providing a prepared slide the fluorescent label is selectedfrom the group consisting of Hoechst 33258, M344 plus a fluorescentconjugate, an anti-HER2/neu protein probe plus a fluorescent conjugate,an anti-EGFR probe plus a fluorescent conjugate, and DNase I plus afluorescent conjugate.
 139. The method of claim 138 wherein in the stepof providing a prepared slide the fluorescent conjugate is selected fromthe group consisting of Texas Red, bodipy and fluorescein.
 140. Themethod of claim 132 wherein the step of providing a prepared slide, thecell sample is collected by washing a body organ, collected from a bodyfluid, or collected from a needle aspiration of a body gland or organ.141. The method of claim 132 wherein the step of outputting the numberis followed by the step of classifying the cell sample as to cancerrisk.
 142. The method of claim 132 wherein the step of classifying thecell sample further comprises using at least one additional variable.143. A method of analyzing a cell sample derived from urine or a bladderwash, comprising:providing a prepared slide, the prepared slide havingbeen prepared by applying a portion of a cell sample to a slide thentreating the slide with a fluorescent label for labeling a cytologicalmarker to form a labeled cytological marker and wherein the portion ofthe cell sample used in preparing the slide was fixated using a fixativecomposition comprising a salt of ethylenediaminetetraacetic acideffective in inhibiting formation of substantially all of the crystalsin the sample; irradiating a portion of the prepared slide with anamount of an excitation wavelength of light effective in causing thefluorescent label in the cell to emit fluorescent light having anemission wavelength for forming a field image; using a microscope meansto select cell images on the field image; classifying the cell image aspositive or negative for the fluorescent label; using a neural netcomputing means to further classify a positive cell image as atrue-positive cell image or as a false-positive cell image, wherein theneural net computing means has been previously trained with a trainingset comprising:a true-positive image set comprising positive cell imagesderived from one or more subjects diagnosed as having cancer, and afalse-positive image set comprising positive cell images derived fromone or more subjects diagnosed as free of cancer; obtaining a parameterrelated to the number of true-positive cell images; and outputting theparameter for use in classifying the cell sample.